# Assessing the reliability of silicone replicas in the analysis of sickle gloss at the early Neolithic site of Cheikh Hassan (Northern Levant, 11th millennium BP)

Fiona Pichon, Juan José Ibañez, Patricia Anderson, Frédéric Abbès

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Fiona Pichon, Juan José Ibañez, Patricia Anderson, Frédéric Abbès. Assessing the reliability of silicone replicas in the analysis of sickle gloss at the early Neolithic site of Cheikh Hassan (Northern Levant, 11th millennium BP). Journal of Archaeological Science, 2025, 185, pp.106446. 10.1016/j.jas.2025.106446. hal-05419666

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# Assessing the reliability of silicone replicas in the analysis of sickle gloss at the early Neolithic site of Cheikh Hassan (Northern Levant, 11th millennium BP)

Fiona Pichon<sup>a,c,*</sup>, Juan José Ibáñez Estevez<sup>a,b</sup>, Patricia Anderson<sup>b,c</sup>, Frédéric Abbès<sup>c</sup>

## ARTICLE INFO

**Keywords:**

Neolithic

Southwest Asia

Harvest

Sickle gloss

Confocal microscopy

Silicone moulds

Flint tools

## ABSTRACT

Over the past decade, confocal microscopy combined with quantitative texture analysis has opened new possibilities for studying prehistoric tool use. However, a key challenge remains in identifying the extent to which different acquisition methods—direct surface measurements versus silicone moulds—influence the textural properties of the original surfaces. This study evaluates whether high-precision polyvinylsiloxane can be reliably used for the identification of plant types harvested with lithic tools during the initial phases of agriculture in Southwest Asia. We compared texture parameters from original surfaces and moulds on eight experimental tools used to cut various plants (domestic, wild and wild cultivated cereals and reeds). Our results show that textures from direct measurements and those from moulds form clearly distinct groups, indicating that the two methods are not interchangeable for harvesting tools. The differences introduced by the moulding process follow a consistent pattern, rather than resulting from random variation. Based on this, we developed a silicone-based experimental model capable of distinguishing between tools used for harvesting different plant types. Applying this model to Pre-Pottery Neolithic glossy tools from Cheikh Hassan confirms a gradual shift from green to ripe cereal harvesting in the Middle Euphrates, in line with findings from neighbouring sites based on direct surface measurements.

## 1. Introduction

The integration of confocal microscopy and quantitative texture analysis into use-wear studies offers significant potential to advance our understanding of prehistoric tool function. The current standard methodology in use-wear analysis, which combines what were formerly termed the low-power (Odell, 1981) and high-power approaches (Keeley, 1980), is based on analogy by comparing wear patterns observed on experimental and archaeological tools (Vaughan, 1985; González Urquijo and Ibáñez Estevez, 1994; van Gijn, 2010; Marreiros et al., 2020). This approach is largely experience-based, as functional interpretations rely on the visual comparison and matching of wear patterns observed on experimental and archaeological specimens.

Over the past decade, the application of confocal microscopy and 3D

texture analysis has gained increasing prominence in use-wear studies, providing a more precise and replicable means of identifying worked materials and assessing the intensity of use (Evans and Donahue, 2008; Evans and Macdonald, 2011; Ibáñez et al., 2014, 2016, 2019; Stemp et al., 2013; Macdonald, 2014; Álvarez-Fernández et al., 2020; Ibáñez and Mazzucco, 2021; Zhang et al., 2024; Pichon et al., 2025a,b; Zupancich et al., 2025). Traditional use-wear analysis relies on direct comparative examination of tool surfaces against experimental and ethnographic reference collections, often involving dozens of images. Visual analysts use these comparisons to identify use-wear patterns and infer function. Quantitative approaches complement this process by providing reproducible, standardized measurements that can be systematically shared and applied across studies. Together, these methods build cumulative knowledge about tool use, each contributing distinct

<footnote><sup>a</sup> Cultural Landscapes, Institución Milá y Fontanals, Spanish National Research Council (CSIC), C/ de les Egipcàques 15, 08001, Barcelona, Spain</footnote>

<footnote><sup>b</sup> CEPAM - Culture et Environnements, Préhistoire, Antiquité, Moyen-Age, UMR 7264, CNRS, Pôle Universitaire Saint Jean d'Angély, SJA 3 - CEPAM UMR7264 - CNRS - UNS, 24, avenue des Diables Bleus, F, 06300, Nice, France</footnote>

<footnote><sup>c</sup> Archéorient - Environnements et sociétés de l'Orient ancien, UMR 5133, CNRS, Maison de l'Orient et de la Méditerranée, 7 rue Raulin, 69365, Lyon, France</footnote>

<footnote>* Corresponding author. Cultural Landscapes, Institución Milá y Fontanals, Spanish National Research Council (CSIC), C/ de les Egipcàques 15, 08001, Barcelona, Spain.</footnote>

<footnote>E-mail address: fiona.pichon@imf.csic.es (F. Pichon).</footnote>
but complementary strengths to the interpretation of archaeological materials. Nevertheless, emerging quantitative approach presents important challenges. Chief among them is the need to understand how the conditions under which confocal 3D acquisitions are obtained affect the outcomes of texture analyses. These factors include the technical specifications of the confocal microscope, the objectives employed and the scale of analysis (Bradfield, 2022; Calandra, 2022; Jiménez-Manchon et al., 2024), as well as the sampling strategies for the selected measured areas (Martisius et al., 2018). In our study, appropriate areas were chosen through qualitative assessment based on our previous experience as use-wear analysts, selecting only well-developed traces associated with harvesting and visually free from taphonomic alteration. This approach ensures that quantitative analysis is conducted on representative surfaces, minimizing subjective judgment while leveraging the analysts' expertise. An equally critical consideration is whether measurements are taken directly on the artifact's surface or on a silicone mould, as this choice can influence the resulting quantitative data. An equally critical consideration is whether measurements are conducted directly on the artifact's surface or on a silicone mould, as this choice can influence the resulting quantitative data (Macdonald et al., 2018; Valtierra et al., 2024; Rodriguez and Iovita, 2025).

The use of high-precision polyvinylsiloxane silicones—originally developed for dental applications—is well established in archaeology, particularly for microwear analysis of animal and human teeth (e.g., Rivals et al., 2009, 2015; Fiorenza et al., 2009; Sánchez-Hernández et al., 2014), as well as for documenting and replicating use-wear traces on lithic, bone tool and shell surfaces (Banks and Kay, 2003; Ollé and Vergès, 2014; Camarós et al., 2016; Martisius et al., 2018; Macdonald et al., 2025). These silicones facilitate the production of single-part moulds applied directly to archaeological materials (Rose, 1983; D'Er-rico, 1988; Camarós et al., 2016), capturing fine microscopic details that can subsequently be analysed using a range of techniques, from low-magnification stereomicroscopy to higher magnification methods such as metallographic, scanning electron, or confocal microscopy (Galbany et al., 2004). Moulds offer several advantages over direct surface scans, particularly when analyzing large objects such as ground stone tools (Zupancich et al., 2025) or human and animal jaws (Martínez et al., 2022), which cannot be accommodated under a microscope. They are also essential for the analysis of objects that cannot be transported to laboratories, enabling the study of fragile or large specimens (Camarós et al., 2016).

However, the extent to which high-precision polyvinylsiloxane moulds alter the textural properties of original surfaces—and consequently affect the capacity to differentiate between various wear patterns—remains insufficiently understood. Despite the widespread use of surface texture analysis, few studies have assessed the fidelity of silicone surface replication from a quantitative perspective.

Goodale et al. (2015) evaluated the accuracy and precision with which silicon-based impression media replicate both rough and smooth surfaces, revealing substantial variation across products of differing viscosities. They compared statistically areal texture data (ISO 25178-2 parameters and Scale Sensitive Fractal Analysis) from sub-micrometre resolution moulds made with various media. Their results highlight significant variation in accuracy and precision depending on the medium's viscosity, surface roughness, and application method. On rough surfaces, high-viscosity silicones showed significant deviations from the original surface—up to 10 parameters differing significantly—while some low-to mid-viscosity compounds produced more faithful replicas (Goodale et al., 2015). To assess the potential impact of replication errors on comparative studies, Goodale et al. (2015) used a case study on dental microwear in two wild fish populations with known dietary differences. They found that, for several texture parameters, the variation introduced by certain impression media exceeded the magnitude of actual differences observed between the dietary groups, highlighting potential risks for comparative studies. Importantly, they concluded that data obtained from different moulding materials are not directly

comparable, reinforcing the need for greater standardization in surface replication protocols.

Macdonald et al. (2018), using laser-scanning confocal microscopy, further highlighted challenges in using moulds for surface metrology, including the loss of fine surface features, artificial smoothing, and reduced variability in deep valleys relative to the originals. While these findings indicate that the intrinsic texture of moulding materials influences surface measurements—particularly on rough surfaces or those with deep striations—these studies did not assess whether moulds produce sufficiently accurate data to enable meaningful comparisons between direct measurements and silicone impressions. This raises a key question: to what extent do high-precision polyvinylsiloxane moulds impact the analytical sensitivity of use-wear discrimination?

This study addresses how the use of moulds affects the identification of plant types harvested with lithic tools during the initial phases of agriculture in Southwest Asia. Over the past decade, we have developed a method for distinguishing among plant types—including green wild cereals, semi-green cultivated wild cereals, ripe domestic cereals, reeds, and grasses—through gloss texture analysis on both experimental and archaeological tools using confocal microscopy and metrology software (Ibáñez et al., 2014). Applying this method, we have analysed a substantial assemblage of over 200 flint sickle blades from Natufian to Late Pre-Pottery Neolithic B (LPPNB) contexts in Southwest Asia (13th to Late 10th mill. BP), revealing a gradual transition from the collection of wild plants to increasingly organized farming systems during the emergence of food production (Ibáñez et al., 2016, 2021; Pichon et al., 2021, 2023). Although the overall trajectory points toward the harvesting of riper, more productive crops, this transition did not follow a uniform or strictly chronological pattern across the region (Ibáñez et al., 2021). Furthermore, we demonstrated the central role of cereal-based agriculture, not only in subsistence but also across multiple technical domains, including construction, pottery making, and livestock management, underscoring its pivotal importance in early Neolithic societies (Ibáñez et al., 2025).

Given that upcoming analyses involve glossy sickle blades for which only high-precision polyvinylsiloxane moulds are available, we conducted a targeted evaluation of how the use of moulds may affect the accuracy of our functional interpretations. We compared the textural parameters of identical areas of harvesting gloss, measured both directly on the original surfaces and on silicone moulds, using eight experimental tools employed in plant cutting. Our results indicate that textures derived from direct measurements form distinct clusters compared to those obtained from moulds, suggesting that, in the case of harvesting tools, direct measurements and moulds are not interchangeable. Further analysis of the textural parameters of both groups revealed that the deformations introduced by the moulding process follow a consistent pattern rather than occurring randomly. Based on these findings, we propose that experimental wear models derived from moulds can be reliably compared to archaeological traces obtained from moulds. To test this hypothesis, we developed a new experimental model capable of differentiating tools used for cutting ripe domestic cereals, semi-green cultivated wild cereals, green wild cereals in natural stands, and reeds, based solely on mould-based measurements following the approach taken in Ibáñez et al., (2016). We then applied this model to the analysis of harvesting tools from the Pre-Pottery Neolithic A and B levels at Cheikh Hassan (Middle Euphrates, Syria). Our study confirms previous results, suggesting that cereal harvesting followed a gradual evolutionary trajectory, transitioning from green to ripe harvesting practices. This conclusion is consistent with data from nearby contemporaneous sites (Tell Mureybet, Dja'de, and Jerf el Ahmar), which were analysed using direct surface measurements (Ibáñez et al. 2016, 2021; Pichon et al., 2021).
## 2. Comparing harvesting gloss from high-resolution silicone mould and flint tools

### 2.1. Method

#### 2.1.1. Experimental framework

The experimental program comprises 8 experimental tools used for harvesting green wild cereals growing in natural stands (*Triticum boeticum* Boiss., *T. dicoccoides* (Korn. ex Asch. & Graebn) Schweinf. and *Hordeum spontaneum* K. Koch), semi-green wild cultivated cereals (*T. boeticum* *thaoudar*), ripe domestic cereals (*T. dicoccum* L. and *T. spelta* L.), and reeds (*Phragmites australis* (Cav.) Trin. ex Steud.) (Table 1; Fig. 1; Supplementary Material File 1).

#### 2.1.2. Silicone moulding

The experimental tools were cleaned with soapy water by gently rubbing the surface with fingers, then dried using a hand dryer to remove any traces of moisture. Subsequently, the active edges were moulded using high-precision polyvinylsiloxane silicone with low viscosity (Provil® Novo Light Fast Set). After 10 min of drying and setting, the silicone moulds were opened using a thin surgical scalpel along the cutting edge, carefully avoiding any contact with the surface edges. For each mould, both parts of the silicone were then cleaned again with soapy water by gently rubbing the surface with fingers before proceeding to analysis with a confocal microscope.

#### 2.1.3. Confocal microscopy and 3D texture measurement

Areas of 650 × 500 µm were acquired using the Sensofar Plu Neox white light scanning confocal microscope, employing an X20 (0.45 NA) objective that provides high resolution of the texture surface. The spatial sampling rate was set at 0.83 µm, with an optical resolution of 0.31 µm, a vertical resolution of 20 nm, and a z-step interval of 0.5 µm.

Acquisitions were taken along the edges of experimental flint tools and directly on the mould replicas of the same edges, focusing on areas exhibiting the most developed micropolish associated with plant harvesting. The same areas were selected on both the flint tools and the mould replicas, with the advantage of being able to observe the images directly on a computer under the objective lens. The number of selected areas ranged from 4 to 7 per tool, resulting in a total of 41 zones of 650 × 500 µm acquired on the flint tools and 41 zones of 650 × 500 µm

acquired on the mould replicas, using the SensoSCAN software.

Confocal microscopes offer precise microtexture measurements but may introduce some system noise. This noise, quantified as the difference between consecutive measurements of the same surface, can be minimised by applying a threshold to the surface measurements, disregarding a certain proportion of the measured area. In this study, 99.5 % of the surface was scanned, while the remaining 0.05 % was not considered.

The 650 × 500 µm 3D images acquired the confocal microscope were processed using the software MountainsMap (v.7) from Digital Surf to extract 3D texture parameters. In the same template, we uploaded the 650 × 500 µm image of the flint and the corresponding 650 × 500 µm image of the mould. We mirrored the mould image along the Z axe to align it with the flint image. First, we extracted the topography layer from both images (flint and mould) in parallel. Using the build series operator, we superposed the two surfaces, and we selected a 300 × 300 µm subsample. Using the shift surface operator, we manually defined two points to shift both the mould and flint surfaces based on their intersection. At this stage, the two images were well aligned, as verified by their profiles. From this, we selected a 200 × 200 µm subsample, which corresponds to the standard size used in our previous methodology to discriminate plant-related cutting polishes (Ibáñez et al., 2014, 2016, 2021, 2025; Pichon et al., 2021, 2023, 2025). Finally, we extracted the surface layers of the flint and mould, ensuring that the same areas will be analysed. Consequently, 60,200 × 200 µm subareas in total were sampled for each category, flint and mould.

A levelling operator based on the Least Squares (LS) Plane Method was applied to correct for the lack of horizontality in the 3D surfaces. To isolate polish texture related to the characteristics of the worked material from the natural irregularities of the flint surface, spatial filtering was employed. This involved moving a small filtering matrix (known as a kernel) across the surface. The arithmetic mean operator was used, averaging each point with its 13 × 13 neighbouring points. After this processing, the texture of the resulting surface—referred to as the Residue surface—was measured. The measuring method consists in selecting 3D surface texture parameters grouped according to ISO standards (including amplitude, spatial, hybrid, functional, feature, and other categories), along with the extraction of additional parameters to characterize the microvalley network. This parameter set was chosen based on its documented ability to extract meaningful textural

<caption>**Table 1**</caption>

The experimental program. For each tool (both flint and mould), the total number of large 3D images (areas of 650 × 500 µm) measured with the microscope confocal, and the total number of subsamples (areas of 200 × 200 µm) selected with the Mountain 7 software from Digital Surf is indicated (refer to section Confocal microscopy and 3D texture measurement).

<table><thead><tr><th rowspan="2">EXP<br>n°</th><th rowspan="2">Worked material</th><th rowspan="2">Year</th><th rowspan="2">Place</th><th rowspan="2">State</th><th rowspan="2">Action</th><th rowspan="2">Hafting</th><th rowspan="2">Time<br>(min)</th><th colspan="2">3D images</th></tr><tr><th>Areas of 650 × 500 µm</th><th>Subsamples of 200 × 200 µm</th></tr></thead><tbody><tr><td>1</td><td>Wild cereals<br><i>T. boeticum</i></td><td>1995–6</td><td>Jebel el Arab (Syria)</td><td>Green</td><td>Cutting<br>low</td><td>Lateral</td><td>780'</td><td>5</td><td>10</td></tr><tr><td>1</td><td>Wild cereals<br><i>H. spontaneum,<br>T. dicoccoides</i></td><td>2009–10</td><td>Jebel el Arab (Syria)</td><td>Green</td><td>Cutting<br>low</td><td>Lateral</td><td>240'</td><td>4</td><td>6</td></tr><tr><td>3</td><td>Wild cultivated<br>cereals<br><i>T. boeticum thaoudar</i></td><td>1989</td><td>Jalès (France)</td><td>Green to semi-<br>green</td><td>Cutting<br>low</td><td>Lateral</td><td>Unknown</td><td>6</td><td>7</td></tr><tr><td>4</td><td>Wild cultivated<br>cereals<br><i>T. boeticum thaoudar</i></td><td>1989</td><td>Jalès (France)</td><td>Green to semi-<br>green</td><td>Cutting<br>low</td><td>Lateral</td><td>Unknown</td><td>6</td><td>13</td></tr><tr><td>5</td><td>Domestic cereals<br><i>T. dicoccum</i></td><td>2021</td><td>Quinzanas, Asturias<br>(Spain)</td><td>Ripe</td><td>Cutting<br>low</td><td>Lateral</td><td>360'</td><td>4</td><td>6</td></tr><tr><td>6</td><td>Domestic cereals<br><i>T. spelta</i></td><td>2021</td><td>Quinzanas, Asturias<br>(Spain)</td><td>Ripe</td><td>Cutting<br>low</td><td>Lateral</td><td>960'</td><td>5</td><td>6</td></tr><tr><td>7</td><td>Reeds<br><i>Phragmites australis</i></td><td>1989</td><td>Jalès (France)</td><td>Ripe</td><td>Cutting<br>low</td><td>Lateral</td><td>Unknown</td><td>5</td><td>5</td></tr><tr><td>8</td><td>Reeds<br><i>Phragmites australis</i></td><td>Unknown</td><td>Syria</td><td>Ripe</td><td>Cutting<br>low</td><td>Lateral</td><td>180'</td><td>6</td><td>7</td></tr></tbody><tfoot><tr><td colspan="6">Total</td><td></td><td>41</td><td></td><td>60</td></tr></tfoot></table>
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Experimental use-wear micropolish from harvesting green wild cereals in natural stands (a, b), semi-green wild cultivated cereals (c, d) and domestic ripe cereals (e, f). Micrographs were taken with a CCD (charge-coupled device) camera attached to a Leica DM 2500 M reflected-light optical microscope.</figcaption></figure>

information and to differentiate between use-wear patterns (Ibáñez et al., 2014, 2016, 2021, 2025; Ibáñez and Mazzucco, 2021; Pichon et al., 2025a,b). A total of 56 areal surface parameters were computed for each subsample, distributed across the following groups (see Supplementary Materials File 2; Leach, 2024): (1) Amplitude parameters, which relate to variations in height across the surface; (2) Spatial parameters, reflecting lateral characteristics along the X and Y axes, often derived from spectral analyses; (3) Hybrid parameters, combining amplitude and spatial features; (4) Functional parameters, derived from the Abbott-Firestone bearing curve; (5) Feature parameters, and (6) Other parameters not fitting into the previous categories. Additionally, once the surfaces were vectorised, three metrics were used to describe the microvalley network by identifying all furrows and calculating their maximum depth, mean depth (MDF), and average density (MDenF).

### 2.1.4. Statistical procedure

Statistical analysis was conducted using the IBM SPSS 29® statistics software package to identify the combination of surface texture parameters that demonstrate significant discriminatory capacity between the experimental groups and to build predictive models using quadratic discriminant analysis (Lix and Sajobi, 2010). To identify the most relevant discriminant 3D texture parameters (called independent variables in the analysis) and to remove the ones that are redundant or not related to group distinction, a stepwise procedure using Wilks' lambda distribution test was performed. The stepwise method starts with a model that does not include any variables. The variable with the minimum Wilks'

lambda and the maximum F to Enter value ($F = 3,84$ as a cut-off) is included in the model first, followed by the next most important variable. At each step, all variables are reviewed to find the one that contributes most to the discrimination between groups and are re-examined to ensure that any variable entered earlier has not become redundant in the presence of recently added variables.

## 2.2. Results

The stepwise discriminant analysis of the eight experimental tools—measured both on moulds and directly on original surfaces, resulting in sixteen groups of measurements—yields a strong classification rate (Table 2). The analysis is based on six textural parameters: Sp, Sal, Vm ($p = 10 \%$), Vvv, Sk, and Smean. The classification graph clearly shows that tools and moulds group differently, with direct measurement scans clustering to the left and mould measurements to the right (Fig. 2). This clustering demonstrates that moulds and direct measurements are classified separately, meaning they cannot be combined in the same analysis. The clustering of mould measurements also suggests that the surface deformation caused by silicone moulding is consistent rather than random. Furthermore, the grouping of each tool within the clusters is relatively similar, as each tool occupies a comparable position relative to the others. This indicates that the discriminant power of the textural parameters is internally consistent for both direct measurements and moulds.

To understand how moulding deforms the original surface, we
<caption>**Table 2**</caption>

Results of classification of the sixteen plant-harvested tool by quadratic discriminant analysis. 90,0 % of originally grouped cases were correctly classified. Shaded cells correspond to correct classifications. The nomenclature is as follows: tools 1 to 8 correspond to original flint surfaces — 1–2: green wild cereals; 3–4: semi-green wild cultivated cereals; 5–6: ripe domestic cereals; 7–8: reeds. Tools 9 to 16 refer to their corresponding silicone moulds — 9–10: green wild cereals; 11–12: semi-green wild cultivated cereals; 13–14: ripe domestic cereals; 15–16: reeds.

<table><thead><tr><th colspan="16">Predictive group membership</th></tr><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>12</th><th>13</th><th>14</th><th>15</th><th>16</th><th>Total</th></tr></thead><tbody><tr><th>Count</th><td>1</td><td>10</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>10</td></tr><tr><th>2</th><td>0</td><td>5</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>6</td></tr><tr><th>3</th><td>0</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>7</td></tr><tr><th>4</th><td>1</td><td>0</td><td>0</td><td>10</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>13</td></tr><tr><th>5</th><td>0</td><td>0</td><td>0</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>6</td></tr><tr><th>6</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>6</td></tr><tr><th>7</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>5</td></tr><tr><th>8</th><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>7</td></tr><tr><th>9</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>7</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>10</td></tr><tr><th>10</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>4</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>6</td></tr><tr><th>11</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>7</td></tr><tr><th>12</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>12</td><td>0</td><td>0</td><td>0</td><td>0</td><td>13</td></tr><tr><th>13</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>6</td></tr><tr><th>14</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>6</td><td>0</td><td>0</td><td>0</td><td>6</td></tr><tr><th>15</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>5</td><td>0</td><td>5</td><td>0</td></tr><tr><th>16</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>7</td><td>7</td></tr><tr><th>%</th><td>1</td><td>100</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>2</th><td>0</td><td>83,3</td><td>0</td><td>16,7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>3</th><td>0</td><td>0</td><td>100</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>4</th><td>7,69</td><td>0</td><td>0</td><td>76,9</td><td>0</td><td>0</td><td>0</td><td>15,4</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>5</th><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>6</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>7</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>8</th><td>0</td><td>0</td><td>0</td><td>28,6</td><td>0</td><td>0</td><td>0</td><td>71,4</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>9</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>70</td><td>0</td><td>0</td><td>10</td><td>10</td><td>0</td><td>0</td><td>10</td><td>100</td></tr><tr><th>10</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>16,7</td><td>66,7</td><td>0</td><td>16,7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>11</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>12</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>7,69</td><td>0</td><td>92,3</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>13</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>14</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td><td>0</td><td>0</td><td>0</td><td>100</td></tr><tr><th>15</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td><td>0</td><td>0</td><td>100</td></tr><tr><th>16</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>100</td><td>0</td><td>100</td></tr></tbody></table>
<figure><img src="image_4.png" /></figure>

Fig. 2. Plots with the two best functions of the quadratic discriminant analysis for the sixteen experimental tools of plant harvesting. Functions 1 and 2 explain 86.6 % of the variance. The nomenclature is as follows: tools 1 to 8 correspond to original flint surfaces — 1–2: green wild cereals; 3–4: semi-green wild cultivated cereals; 5–6: ripe domestic cereals; 7–8: reeds. Tools 9 to 16 refer to their corresponding silicone moulds — 9–10: green wild cereals; 11–12: semi-green wild cultivated cereals; 13–14: ripe domestic cereals; 15–16: reeds. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

analysed 56 textural parameters (Supplementary Material File 3). We focused on 12 parameters for which the values are consistently higher or lower in the direct measurements compared to the mould for the height

tools for the eight experimental tools. The parameters showing this distinction include (Supplementary Material File 4): Vm (p = 10 %), Vvv, Sk, Spk (Gaussian filter, 0.08 mm), Svk (Gaussian filter), S5p (pruning = 5 %), S5v (pruning = 5 %), Smr1 (Gaussian filter, 0.08 mm), Sal, Spc (pruning = 5 %), Sbi, and Smean.

For the volume-related parameters (Vm and Vvv; Fig. 3), these measure the distribution of heights, distinguishing the surface core from the peaks (Vm-p) and the voids (Vvv; Fig. 3). The differentiation between these parameters occurs at the 10th and 80th percentiles of the measurements. Vm(p) captures the 10 % of the highest measurements, while Vvv accounts for 20 % of the lowest measurements. Vm(p) is consistently higher (44.9 %) in the direct measurements of the height tools, whereas Vvv is higher (23.5 %) in the moulds.

When comparing the height parameters—Sk, Spk, and Svk—the results are consistent and coherent. Sk measures the core of the surface, Spk evaluates the roughness depth of the peaks, and Svk assesses the roughness depth of the valleys. Spk is consistently higher (36.2 %) in direct measurements, while Sk (26.2 %) and Svk (23.6 %) are higher in the moulds. This indicates that peaks are systematically underestimated, while voids and valleys are overestimated in the moulds.

Other parameters further support the tendency of moulds to underestimate the higher part of the texture, such as S5p, S5v, and Smr1. S5p measures the average height of the five highest peaks within the defined area, while S5v does the same for the five deepest valleys. S5p shows higher values in direct measurements (28.6 %), while S5v is deeper in moulds (18.2 %). Smr1, which measures the upper material ratio, is more pronounced in direct measurements (27.5 %).

The comparison of the same surface after direct measurement and mould measurement visually illustrates how moulding affects surface characteristics (Fig. 4). On the mould surface, the upper parts are more extended (represented by white and red colours), as the highest peaks of the original surface are truncated, resulting in a more regularized upper surface. Conversely, the lower areas (in blue) are also more extended.

Spc quantifies the curvature of peaks that exceed a threshold (pruning = 5 %) in a surface texture. A smaller value indicates that the points of contact with other objects are rounded, while a larger value suggests that these contact points are more pointed. Surfaces directly

<figure><img src="image_5.png" /></figure>

Fig. 3. Representation of volume-related textural parameters. Image adapted from the Digital Surf user manual.
<figure><img src="image_6.png" /><figcaption>Fig. 4. Comparison of surface measurements taken directly on flint and from the corresponding silicone mould for cereal domestication (top) and reed cutting (bottom). These measurements correspond to DOMESTIC_5.1_Flint/DOMESTIC_5.1_Silicone and REEDS_7.5_Flint/REEDS_7.5_Silicone in Supplementary Material_File 4.</figcaption></figure>
measured show higher results (46.3 %) than the moulds, indicating that
moulding not only reduces the number of peaks but also makes them
appear less pointed. Moulding also leads to lower kurtosis in the dis-
tribution of heights (Sku 33.3 % lower), lower frequencies of wave-
lengths (SaL 14.5 % higher), and a higher mean height of the surface
(Smean 91.9 %).

3. A new quantitative model for harvesting gloss texture analysis on high-resolution silicone moulds

High-magnification use-wear studies of sickle gloss have highlighted
changes in micropolish texture over time, spanning from the 13th to the
8th millennia cal BP in Southwest Asia. Earlier tools, such as those from
the Natufian period, generally exhibit a more domed and smooth polish,
whereas later Neolithic examples tend to show flatter, and more abraded
polish. This diachronic evolution has been linked to shifting strategies
for crop acquisition, themselves tied to the gradual morphological
transformation of cereals during cultivation and domestication, with
increasingly mature plants being reaped to optimise yield efficiency
(Anderson, 1992; Unger-Hamilton, 1992; Ibáñez et al., 1998). Indeed,
experimentations indicate that, to avoid premature seed dispersal, wild
cereals with brittle rachises were likely harvested while still green,
resulting in a domed, smooth, and bright micropolish. In contrast, do-
mestic cereals — characterised by tougher spikelets that remain
attached even when fully mature — could be harvested at a more
advanced stage of ripeness, generating a flatter and more abraded
micropolish (Anderson, 1992). Furthermore, to balance yield optimi-
sation without excessive grain loss, wild cultivated cereals appear to
have been harvested in a semi-ripe state, when most of the crop was
mature enough, though some grains remained attached to the ear
(Anderson, 1992).

Microscopic textural features of sickle gloss can provide insights into
the moisture level of stems at the time of harvesting, allowing distinc-
tions between green wild, semi-green wild cultivated, and ripe domestic
cereals (Anderson, 1991, 1992; Unger-Hamilton, 1992; Anderson and
Valla, 1996). However, differentiating micropolish produced by various
plant types — especially across different stages of cereal maturity — has
remained challenging when relying solely on traditional optical micro-
scopy. In addition, other siliceous plants such as reeds also generate
macroscopic gloss (Vaughan, 1985; Jensen, 1994; Anderson, 1994) and
were extensively used during the Neolithic for subsistence, craft activ-
ities and construction (Rosen, 1992; Flohr et al., 2015; Douché and
Pichon, 2018; Whitlam and Weide, 2023; Tsartsidou et al., 2025). To
overcome these limitations, we developed a novel methodology ground-
ded in a robust experimental framework to quantify gloss micro-texture
and more accurately identify the plant type harvested with archaeo-
logical sickles (Ibáñez et al., 2014, 2016, 2021). We have previously
applied this methodology to the analysis of harvesting tools by directly
measuring gloss texture using a confocal microscope. However, trans-
porting archaeological tools to laboratories equipped with confocal
microscopes is not always feasible, due to restrictions on the expatria-
tion of heritage items. For this reason, the use of high-resolution moulds
for documenting use-wear traces becomes essential. Consequently, it is
crucial to develop new experimental use-wear models based on texture
analysis that enable the identification of use-wear patterns on archae-
ological moulds. The choice of silicone moulds over positive resin casts
was motivated by their proven reliability for capturing microtopo-
graphic details without introducing additional artefacts. If moulding
introduces some bias in the textural characteristics of the surface, as
demonstrated in this paper, then an additional step in surface manipu-
lation — making a positive resin cast from the mould — would most
likely introduce further deformations to the cast surface compared to the
original. In addition, silicone moulds are non-toxic and easier to handle,
whereas resin materials can release harmful compounds during prepa-
ration and hardening, making silicone a safer and more practical option
for repeated sampling in laboratory environments.

To advance this line of research, the new quantitative model we have
developed—using high-resolution silicone moulds—focuses on dis-
tinguishing between different types of plants harvested.

3.1. Discriminating between harvested plant types

To distinguish between the types of harvested plants, the experi-
mental program comprises twenty tools replication of sickles used as
follow: five for ripe domestic cereals (T. dicoccum L., T. spelta L., T.
monococcum L., Secale cereale L.), seven for semi-green wild cultivated
cereals (T. boeticum thaoudar), five for green wild cereals growing in
natural stands (T. boeticum Boiss., H. spontaneum K. Koch, T. dicoccoides
(Korn. ex Asch. & Graebn) Schweinf.), and four for reeds (Phragmites
australis (Cav.) Trin. ex Steud.) (Supplementary Material_File 5). Sickles
with wooden and antler handles were used, with fine-grained flint and
obsidian inserts. Cereals were mostly reaped close to the ground (around
20 cm), except for two tools employed for reaping wild cultivated ce-
reals and domestic cereals near the ear.

The same procedure described above was followed for cleaning and
moulding the experimental tools, as well as for the preparation of high-
precision polyvinylsiloxane silicone moulds for analysis. Then, the
methodological approach applied in this paper follows the one proposed
in previous studies (Ibáñez et al., 2014, 2016, 2021; Pichon et al., 2021,
2023). For each experimental tool, both the original flint surface and its
corresponding silicone mould were analysed to compare the two data-
sets. Between 9 and 20 areas of 650 × 500 µm were measured along the
edge and the selected samples correspond to areas where micropolish
was most developed (around 90 % of the sampled surface). Analyses
were performed with a Sensofar Plu Neox scanning confocal microscope,
using a 20 × (0.45 NA) objective, a spatial sampling of 0.83 µm, an
optical resolution of 0.31 µm, a vertical resolution of 20 nm, and a z-step
interval of 1 µm.

The 3D images generated by the confocal microscope were processed
using Mountain 7 software from Digital Surf to extract ISO 25178
texture measurement parameters. Subsamples of 200 × 200 µm showing
homogeneous and well-developed micropolish without irregularities
caused by the natural surface of the flint were taken from the 650 × 500
µm areas. A total number of 5–52 subsamples were taken for each tool.
The topography layer was extracted, mirrored along its Z axis, levelled
using the Least Squares (LS) Plane Method, and the form was removed
using a second-degree polynomial. To separate the polish texture asso-
ciated with the characteristics of the worked material from the irregu-
larities of the moulded flint surface, a Gaussian spatial filter was applied.
This filter works by moving a small filtering matrix (called a kernel
matrix) over the surface. The arithmetic mean operator averages each
point with its 13 × 13 neighbouring points. The microtexture to be
measured was then calculated by subtracting the filtered surface from
both the moulded and the flint surfaces.

Surface texture parameters were extracted both from the flint sur-
faces and from their silicone moulds from the silicone moulds, and the
combination of predictors offering the highest discriminatory capacity
among the four experimental groups was selected through a stepwise
procedure, using the IBM SPSS 29® statistical software package. The
results obtained for each image are presented in Supplementary Mate-
rial_File 6_Sheets 1 and 2 and summarised in Table 3, allowing a direct
comparison between the two models. The same set of fourteen ISO pa-
rameters was calculated for both datasets: 1) Amplitude parameters,
describing the distribution the distribution of heights (Sq: Root mean
square height; Sds: Density of summits); 2) Spatial parameters quanti-
fying lateral surface information (Sal: Fastest decay autocorrelation; Str:
Texture-aspect ratio; Std: Texture direction index); 3) Hybrid parameters
combining amplitude and spacing (Sdr: Developed interfacial area
ratio); 4) Functional parameters describing surface interaction (Smr2:
Lower bearing area); 5) Functional indices characterizing surface zones
involved in lubrication, wear, and contact phenomena (Sci: Core fluid
retention index; 5) Volume Functional Parameters quantifying material
<caption>**Table 3**</caption>

Predicted group membership through quadratic discriminant classification of the experimental sample. A total of 61,3 % of the original grouped cases were correctly classified for the silicone mould dataset, and 67,3 % for the flint dataset.

<table><thead><tr><th colspan="6">Predictive group membership in silicone moulds</th></tr><tr><th></th><th></th><th>Ripe cereals</th><th>Semi-green cereals</th><th>Green cereals</th><th>Reeds</th><th>Total</th></tr></thead><tbody><tr><td>Count</td><td>Ripe cereals</td><td>85</td><td>20</td><td>17</td><td>37</td><td>159</td></tr><tr><td></td><td>Semi-green cereals</td><td>28</td><td>116</td><td>6</td><td>24</td><td>174</td></tr><tr><td></td><td>Green cereals</td><td>17</td><td>9</td><td>60</td><td>21</td><td>107</td></tr><tr><td>%</td><td>Reeds</td><td>19</td><td>13</td><td>6</td><td>82</td><td>120</td></tr><tr><td></td><td>Ripe cereals</td><td>53,5</td><td>12,6</td><td>10,7</td><td>23,3</td><td>100,0</td></tr><tr><td></td><td>Semi-green cereals</td><td>16,1</td><td>66,7</td><td>3,4</td><td>13,8</td><td>100,0</td></tr><tr><td></td><td>Green cereals</td><td>15,9</td><td>8,4</td><td>56,1</td><td>19,6</td><td>100,0</td></tr><tr><td></td><td>Reeds</td><td>15,8</td><td>10,8</td><td>5,0</td><td>68,3</td><td>100,0</td></tr></tbody></table>

<table><thead><tr><th colspan="6">Predictive group membership in flint surfaces</th></tr><tr><th></th><th></th><th>Ripe cereals</th><th>Semi-green cereals</th><th>Green cereals</th><th>Reeds</th><th>Total</th></tr></thead><tbody><tr><td>Count</td><td>Ripe cereals</td><td>97</td><td>19</td><td>19</td><td>4</td><td>139</td></tr><tr><td></td><td>Semi-green cereals</td><td>26</td><td>124</td><td>18</td><td>11</td><td>179</td></tr><tr><td></td><td>Green cereals</td><td>8</td><td>16</td><td>60</td><td>10</td><td>94</td></tr><tr><td>%</td><td>Reeds</td><td>6</td><td>8</td><td>11</td><td>40</td><td>65</td></tr><tr><td></td><td>Ripe cereals</td><td>69,8</td><td>13,7</td><td>13,7</td><td>2,9</td><td>100,0</td></tr><tr><td></td><td>Semi-green cereals</td><td>14,5</td><td>69,3</td><td>10,1</td><td>6,1</td><td>100,0</td></tr><tr><td></td><td>Green cereals</td><td>8,5</td><td>17,0</td><td>63,8</td><td>10,6</td><td>100,0</td></tr><tr><td></td><td>Reeds</td><td>9,2</td><td>12,3</td><td>16,9</td><td>61,5</td><td>100,0</td></tr></tbody></table>

volume (Vm: Material volume; Vmc: Core material volume; Vvc: Core void volume); 6) *Other parameters* (Smean: Mean height in absolute); 7) *Micro-valley network parameters*, obtained after vectorization of the surface (MDepthF: Mean depth furrows; MDenF: Mean density furrows).

Quadratic discriminant functions were calculated for the 3D texture parameters selected through the stepwise procedure and used to build a predictive model for group membership based on Bayes' theorem. These parameters accurately classified 54,8 % of the 3D experimental samples into their correct group (Table 3). When applied to the flint model, the same parameters achieved a higher classification rate of 67,3 %.

Tools were classified into functional groups using a single-step procedure across all experimental categories (ripe, semi-ripe, and green cereals, reeds), following the refinement of previous protocols (Ibáñez et al., 2014, 2016; Pichon et al., 2021). To evaluate classification reliability, we defined a threshold: an experimental tool is confidently assigned to a functional group if at least 55 % of its 3D image samples fall into the same category, as the refined protocol applied in recent studies (Ibáñez et al., 2021; Pichon et al., 2023, 2025). Out of 20 experimental tools, 14 were correctly identified in the silicone model and 13 in the flint model (Supplementary Material_File 7_Sheets 1 and 2). One tool used for harvesting semi-green wild cultivated cereal was misclassified as ripe domestic cereals in the silicone model, whereas no misclassifications occurred in the flint model.

The same 14 ISO parameters were applied to the 3D surface measurements of the archaeological samples.

# 4. Testing the model in the PPNA-PPNB site of Cheikh Hassan

## 4.1. Cheikh Hassan: a late PPNA to early PPNB Neolithic settlement in Northern Levant (Euphrates valley, Syria)

Cheikh Hassan is a settlement located ca. 100 km east of Aleppo on the left bank of the Middle Euphrates, in an area now submerged beneath the waters of Lake Assad, formed by the Tabqa Dam (Fig. 5). The site is a conical tell, rising more than 12 m above the surrounding alluvial terrace and extending about 450 m east to west. No radiocarbon dates are available at Cheikh Hassan; its chronological attribution relies on stratigraphic correlations and associated material culture. Initially identified during surveys in 1963 by A. Rihaoui, who recognised material from the Hellenistic, Roman and Islamic periods (Rihaoui, 1965), the Neolithic occupation was identified one year later, in 1964, by M.N. van Loon on the western flank of the tell. The site was excavated in 1974 and 1976 by Jacques Cauvin, as part of the French Mission of El Kowm-Mureybet and within the framework of the International Campaign to Save the Monuments of the Euphrates (Cauvin, 1977, 1980).

Cauvin's soundings revealed contemporaneous occupation levels of phases IIIA and IIIB at Mureybet, corresponding to the end of the PPNA and the transition to the PPNB (Évin and Stordeur, 2008), characterized by a successive architectural progression from small square cells to rectangular cells with large stone walls (Cauvin, 1978, 1980). The site was subsequently taken over in between 1984 and 1997 by the team of Professor J. Boese from the University of Saarbrücken to excavate the Uruk and post-Uruk levels (Boese, 1995; Muller-Neuhof, 2006). In 1993, a campaign directed by D. Stordeur led to the excavation of the early PPNB levels at the centre of the tell (for a total of total of 125 m²; Stordeur, 1999), contemporary with Mureybet IVA (Évin and Stordeur, 2008) and Dja'de el-Mughara III (Coqueugniot, 1998), dating to the second half of the 8th millennium BCE. The architecture includes rectangular dwellings with walls made of earth and stone (Stordeur, 1999), situated near a lithic workshop dedicated to blade production (Abbès et al., 2001).

Corresponding to the Late PPNA and the Early PPNB, these levels mark the emergence of the first village communities and the transition to early farming practices in the Middle Euphrates, which laid the foundations for food-producing economies based on cultivation and herding (Stordeur and Abbès, 2002; Stordeur and Ibáñez, 2008). In addition, analysis of the workshop assemblage led to the definition of predetermined bipolar blade production — a key technological innovation that came to characterize lithic industries across the Levant during the Neolithisation process (Abbès, 2003).

The village is situated in the area with mean annual rainfall between 200 and 250 mm. The local environment, alluvial plain and steppe hinterland, constituted a rich ecological niche with complementary resources. No archaeobotanical sampling was conducted on the site during the 1976 excavations. However, when excavations resumed in 1993, sampling was carried out, but studies are still ongoing, and no data is currently available. The fauna is represented by animals hunted both in the Euphrates Valley (aurochs, fallow deer, pigs) and in the surrounding steppe areas (wild goats, gazelles, equids; Helmer et al., 1998; Gourichon, 2004). Preliminary analysis indicates that the populations of Cheikh Hassan did not engage in animal husbandry but instead fully exploited their dual environment, hunting a diverse range of game using various practices (Helmer et al., 1998; Gourichon, 2004).

Lithic artefacts found in these layers reflecting the Late PPNA and EPPNB lithic technology as known at Jerf el Ahmar and at Mureybet IIIB and IVA (Abbès, 1994, 2003). Bipolar and unipolar blade production, using respectively fine-grained Eocene flint and fluvial flint of highly variable texture quality, are characteristic of the assemblage (Abbès, 1994). Obsidian is imported from both Central Anatolia, i.e. Göllü Dağ, southern Cappadocia, and Eastern Anatolia, i.e. Bingöl (Abbès et al., 2001). Apart from numerous retouched blades, the most frequent tool
<figure><img src="image_7.png" /><figcaption>Fig. 5. Localisation of the archaeological sites mentioned in the text. © 2025 Luis Cesar Teira Mayolini.</figcaption></figure>

types are projectile tanged points — produced on high-quality pre-determined central blades as well as on technical by-products of comparable quality from bipolar debitage — and transverse burins (Abbès, 2003). Tanged blades, notched blades, blades with lateral “ergot”, burins on fractures, end-scrapers, borers, truncated blades, and glossy

blades complete the toolkit. The same types of tools and projectile points are found on blades from both bipolar and unipolar production systems, indicating that similar tool requirements were addressed through both *chaînes opératoires* (Abbès, 2003).

<figure><img src="image_8.png" /><figcaption>Fig. 6. Plant-cutting microwear identified on glossy blades from the Late PPNA (a–c) and Early PPNB (d–e) levels at Cheikh Hassan. Micrographs were taken with a CCD (charge-coupled device) camera attached to a Leica DM 2500 M reflected-light optical microscope.</figcaption></figure>
## 4.2. Studied sample

The studied material is stored at the archaeological reserves of the French El Kowm Mission (MPK) at the Jalès Center in France (CNRS/ University Lyon 2 laboratory). The previous directors of the MPK, J. Cauvin and D. Stordeur, obtained permission from Syria's Directorate-General of Antiquities and Museums to export flint artefacts to France. All the artefacts and samples analysed and reported in this paper comply with the Valletta Convention and were legally exported.

The glossed sample includes 24 blades from the PPNA levels (Cauvin's excavations) and 6 blades from the Early PPNB levels (Stordeur's excavations, squares FK, JL, and AH), for a total of 30 sickle blades. The sample was selected through observation under an Olympus BHM reflected-light optical microscope (100 ×, 200 ×, 400 ×) to identify microwear patterns related to plant cutting (Vaughan, 1985; González Urquijo and Ibáñez Estévez, 1994; Jensen, 1994, Fig. 6). The use-wear traces were well preserved, with minimal post-depositional alteration. Deep, oblique, and randomly oriented striations were observed, along with some areas of abrasion—features that guided the selection of these blades.

## 4.3. Gloss microtexture analysis through confocal microscopy and metrology software

All 30 archaeological specimens were analysed through high-resolution silicone moulds, not directly. The same procedure as previously described was applied to clean and mould the glossy edges of the archaeological tools, while carefully avoiding any black/brown residues of hafting observed on some of them. The high-precision polyvinylsiloxane silicone moulds were exported to Spain, to be analysed in the Mila y Fontanals Institute of the CSIC with the Sensofar Plu Neox scanning confocal microscope. Following the methodology described above for the experimental program, the 3D images of the archaeological tools obtained with the confocal microscope were classified into the different groups of experimental tools through quadratic discriminant analysis to determine the type of harvested plants (Supplementary Material File 8, Sheets 1 and 2 column C).

First, the results of the texture analysis conducted on 1035 sub-areas on sickle inserts from the PPNA, show a clear dominance of gloss associated with the harvesting of semi-green wild cultivated cereals,

<caption>**Table 4**</caption>

Results of the quadratic discriminant analysis of the experimental tools used for reaping domestic ripe cereals, semi-green wild cultivated cereals, green wild cereals and reeds compared with the archaeological tools from the Late PPNA layers at Cheikh Hassan. 61,3 % of the original cases are correctly classified.

<table><thead><tr><th colspan="6">Predictive group membership</th></tr><tr><th></th><th></th><th>Ripe cereals</th><th>Semi-green cereals</th><th>Green cereals</th><th>Reeds</th><th>Total</th></tr></thead><tbody><tr><td rowspan="6">Count</td><td>Ripe cereals</td><td>85</td><td>20</td><td>17</td><td>37</td><td>159</td></tr><tr><td>Semi-green cereals</td><td>28</td><td>116</td><td>6</td><td>24</td><td>174</td></tr><tr><td>Green cereals</td><td>17</td><td>9</td><td>60</td><td>21</td><td>107</td></tr><tr><td>Reeds</td><td>19</td><td>13</td><td>6</td><td>82</td><td>120</td></tr><tr><td>Cheikh Hassan PPNA</td><td>289</td><td>416</td><td>134</td><td>196</td><td>1035</td></tr><tr><td>%</td><td>53,5</td><td>12,6</td><td>10,7</td><td>23,3</td><td>100,0</td></tr><tr><td rowspan="6">%</td><td>Ripe cereals</td><td>53,5</td><td>12,6</td><td>10,7</td><td>23,3</td><td>100,0</td></tr><tr><td>Semi-green cereals</td><td>16,1</td><td>66,7</td><td>3,4</td><td>13,8</td><td>100,0</td></tr><tr><td>Green cereals</td><td>15,9</td><td>8,4</td><td>56,1</td><td>19,6</td><td>100,0</td></tr><tr><td>Reeds</td><td>15,8</td><td>10,8</td><td>5,0</td><td>68,3</td><td>100,0</td></tr><tr><td>Cheikh Hassan PPNA</td><td>27,9</td><td>40,2</td><td>12,9</td><td>18,9</td><td>100,0</td></tr></tbody></table>

representing 40.2 % of the classified surfaces, which decreases in the Early PPNB to 38,8 % (Tables 4 and 5). Notably, the proportion of ripe domestic cereal gloss increases from 27,9 % in the Late PPNA to 52,0 % in the Early PPNB, while gloss from green wild cereals and reeds decrease drastically.

The number of 3D surface samples analysed per sickle element varies between 5 and 14, introducing a potential bias in the overall results, as tools with more sampled surfaces may disproportionately influence the outcome. To account for this disparity, a weighted average was calculated (Supplementary Material File 9: Average percentage for each predictive membership). The distribution of plant-cutting groups remains largely consistent, whether based on the total number of individual samples or on the weighted average of group percentages per tool.

In addition to assessing the overall results based on all sampled 3D surfaces, we also calculated the results for each archaeological sickle element (Supplementary Material File 9). Following our previous methodology (Ibáñez et al., 2016), we applied a 55 % threshold for classification: a tool is considered correctly classified when more than 55 % of its 3D surfaces fall within a single group. If no group reaches this threshold, the tool is considered unclassified. In the Late PPNA levels, correctly classified sickles are mainly associated with the harvesting of semi-green wild cultivated cereals (n = 7), green cereals (n = 1) and reeds (n = 2), while 14 tools remain are unclassified. These unclassified tools were likely used to cut multiple plant types, resulting in mixed gloss textures. When considering all 3D surfaces classified as cutting ripe, semi-ripe, and green cereals, the dominant use-wear pattern is clearly linked to cereals rather than reeds (based on the 55 % threshold). This suggests that most tools were primarily used for cereal harvesting, except for two tools (CH 3039 and CH 3360) which were used to cut reeds (Supplementary Material File 9). In the Early PPNB levels, the trend shifts slightly: three sickles are classified as having harvested semi-green wild cultivated cereals, while two sickle are classified as having harvested ripe domestic cereals for the first time.

<caption>**Table 5**</caption>

Results of the quadratic discriminant analysis of the experimental tools used for reaping domestic ripe cereals, semi-green wild cultivated cereals, green wild cereals and reeds compared with the archaeological tools from the Early PPNB layers at Cheikh Hassan. 61,3 % of the original cases are correctly classified.

<table><thead><tr><th colspan="6">Predictive group membership</th></tr><tr><th></th><th></th><th>Ripe cereals</th><th>Semi-green cereals</th><th>Green cereals</th><th>Reeds</th><th>Total</th></tr></thead><tbody><tr><td rowspan="6">Count</td><td>Ripe cereals</td><td>85</td><td>20</td><td>17</td><td>37</td><td>159</td></tr><tr><td>Semi-green cereals</td><td>28</td><td>116</td><td>6</td><td>24</td><td>174</td></tr><tr><td>Green cereals</td><td>17</td><td>9</td><td>60</td><td>21</td><td>107</td></tr><tr><td>Reeds</td><td>19</td><td>13</td><td>6</td><td>82</td><td>120</td></tr><tr><td>Cheikh Hassan EPPNB</td><td>118</td><td>88</td><td>11</td><td>10</td><td>227</td></tr><tr><td>%</td><td>53,5</td><td>12,6</td><td>10,7</td><td>23,3</td><td>100,0</td></tr><tr><td rowspan="6">%</td><td>Ripe cereals</td><td>53,5</td><td>12,6</td><td>10,7</td><td>23,3</td><td>100,0</td></tr><tr><td>Semi-green cereals</td><td>16,1</td><td>66,7</td><td>3,4</td><td>13,8</td><td>100,0</td></tr><tr><td>Green cereals</td><td>15,9</td><td>8,4</td><td>56,1</td><td>19,6</td><td>100,0</td></tr><tr><td>Reeds</td><td>15,8</td><td>10,8</td><td>5,0</td><td>68,3</td><td>100,0</td></tr><tr><td>Cheikh Hassan EPPNB</td><td>52,0</td><td>38,8</td><td>4,8</td><td>4,4</td><td>100,0</td></tr></tbody></table>
# 5. Discussion

## 5.1. Methodological implications of silicone mould-based texture analysis in lithic use-wear studies

Silicone-based impression media have become increasingly popular in archaeology, particularly in lithic use-wear analysis, over the past two decades due to their ease of use, non-destructive nature, and relatively low cost. In parallel, the use of confocal microscopy and quantitative standards for characterizing microwear polish on stone tools has expanded rapidly over the past decade and has also been applied to the analysis of silicone moulds (Mazzucco et al., 2022; Paixão et al., 2022; Zupancich et al., 2025). However, the impact of high-resolution moulding and moulding on quantitative results from textural analysis has been little studied (e.g., Goodale et al., 2015; Macdonald et al., 2018; Rodriguez and Iovita, 2025), and the fidelity of replication—which directly affects data quality and measurement accuracy—has so far been poorly assessed.

Differences in replication fidelity between impression media can compromise comparability across studies using different materials. Until now, few studies have directly compared the same set of experimental tools measured both on their original flint surfaces and on their corresponding moulds to quantify the variation in resulting textures. Our study fills this gap by establishing two quantitative models – one based on direct measurements on flint surfaces and one derived from high-resolution silicone moulds – and by comparing their classification performance across the same experimental dataset. The results confirm that, although high-precision polyvinylsiloxane moulds can be effectively integrated into use-wear analysis via confocal microscopy, they introduce measurable and systematic distortions to surface textures. Importantly, these deformations are not random: they consistently affect the same textural parameters in the same direction across different tools. We identified a subset of parameters that showed consistent directional differences, especially on the height measuring tools. Notably, volume-related parameters (Vm, Vvv), surface height parameters (Sk, Spk, Svk), peak and valley extremes (S5p, S5v), and peak shape (Spc) exhibited systematic deviations. For example, the volume above the core (Vm) and peak height (Spk) were consistently higher in direct measurements, whereas valley volume (Vvv), core height (Sk), and valley depth (Svk) were greater in moulds—suggesting that peaks are flattened and valleys exaggerated in the moulds. These distortions also manifested as reduced peak sharpness (lower Spc), decreased kurtosis (Sku), and shifts in frequency (Sal) and mean surface height (Smean), indicating a general smoothing and regularization of surface topography through moulding. Despite these deformations, the discriminant model based on silicone moulds correctly classified around 62 % of the experimental samples – demonstrating that, although precision is lower than direct measurements on flint (67 %), mould-based texture analysis remains a robust and reproducible approach for distinguishing between plant types.

Nevertheless, our results also highlight a critical limitation: moulds and direct measurements cannot be mixed within the same discriminant analysis or reference model. The systematic shift in values—such as the underrepresentation of peak sharpness and the overestimation of valley depth—modifies the overall distribution of the data, leading to separate clustering in between gloss texture measured in flint surfaces and the ones measured on silicone moulds. This means that gloss texture data obtained through moulding are not directly comparable to those collected from original flint surfaces. This issue must be carefully considered when building or expanding reference collections, as combining the two types of data would undermine the reliability of functional interpretations.

Despite these limitations, mould-based analysis holds significant advantages in archaeological contexts where direct scanning is impractical or impossible. This includes large or heavy tools that do not fit under confocal microscopes, as well as museum-held artefacts or those inaccessible to confocal facilities. Moulding allows researchers to

bring a high-fidelity replica into the lab while preserving the original object intact. The non-destructive nature of this approach is particularly valuable for heritage conservation and museum-based research, opening new avenues for functional analysis without compromising preservation requirements.

From a methodological standpoint, our comparative approach demonstrates that mould-based datasets can be used independently to construct reliable predictive models, provided that all data – experimental and archaeological – are acquired under identical conditions. Whenever conditions permit, we strongly recommend conducting measurements directly on tool surfaces. Direct scanning yields more accurate representations of surface microtopography, especially in parameters critical for differentiating between plant types (e.g., Spk, S5p, Spc). These measurements are more sensitive to use-wear variability and therefore better suited for refining experimental models or producing high-resolution datasets for future comparative studies. Direct data acquisition also avoids the dampening and smoothing effects introduced by the moulding process, allowing for more nuanced interpretations of tool use and wear intensity.

In summary, high-resolution moulding with polyvinylsiloxane provides a reliable alternative when direct scanning is not feasible, enabling access to otherwise unreachable archaeological materials. But from a methodological standpoint, direct measurement remains the gold standard and should be prioritized whenever possible. Given the impact of moulding on the quantitative properties of surface textures, it is essential to clearly specify whether measurements were taken on original surfaces or on moulds when sharing data within the scientific community. This distinction is critical for ensuring reproducibility and for allowing meaningful comparisons between studies. To advance cumulative knowledge in quantitative use-wear analysis, datasets should be shared alongside detailed metadata including acquisition settings, equipment used, surface preparation method, and whether measurements were performed on direct surfaces or silicone replicas. Encouraging open-access repositories of reference gloss textures—explicitly labelled with this information—would support training, foster collaboration, and help standardize methodologies across laboratories. Such transparency is equally relevant to other applications of surface metrology in archaeology and beyond.

## 5.2. Cheikh Hassan in the context of plant-harvesting practices in the Middle Euphrates

The absence of archaeobotanical data from Cheikh Hassan limits our ability to identify the plant taxa involved and to directly link harvesting practices with local subsistence strategies. Despite this limitation, the high-resolution texture analysis of sickle gloss provides an independent line of evidence that can be compared with a robust regional dataset from previous studies (Ibáñez et al., 2016, 2021; Pichon, 2017), allowing us to place Cheikh Hassan within broader trends in cereal exploitation along the Middle Euphrates. The Cheikh Hassan tools, analysed through high-resolution silicone mould analysis, were compared with 98 sickles analysed via direct surface measurements from other Middle Euphrates sites (Abu Hureyra, Mureybet, Dja'de, and Halula; Table 6). All tools were classified into four groups of plant-cutting groups using the same 3D texture and statistical methodologies (Ibáñez et al., 2016; Pichon et al., 2021), enabling a consistent assessment of harvesting practices and changes in cereal exploitation through time (Table 7 and Fig. 7). Future integration with the forthcoming archaeobotanical results will be essential to refine and test these interpretations.

In the Late Natufian levels at Abu Hureyra and Mureybet, sickle gloss primarily corresponds to the cutting of green and semi-green cereals. Archaeobotanical evidence remains limited and debated. At Tell Abu Hureyra (13,100–12,200 BP), the main plants exploited included einkorn, rye, various pulses, fruits, and nuts (Hillman, 2000; Colledge and Conolly, 2010; Arranz-Otaegui and Rots, 2023). Around 12,900 BP, the Younger Dryas brought drier conditions, reducing the availability of
<table><caption>**Table 6**

Archaeological tools sampled from the Midde Euphrates area.</caption><thead><tr><th>Period</th><th>Sites</th><th>Chronology</th><th>Area</th><th>Tools</th></tr></thead><tbody><tr><td>Late Natufian</td><td>Abu Hureyra</td><td>13th mill. cal BP</td><td>Middle Euphrates</td><td>4</td></tr><tr><td>Late Natufian</td><td>Mureybet</td><td>Late 13th to Early 12th mill. cal BP</td><td>Middle Euphrates</td><td>3</td></tr><tr><td>PPNA</td><td>Mureybet III</td><td>Mid 12th to Early 11th mill. cal BP</td><td>Middle Euphrates</td><td>14</td></tr><tr><td>PPNA</td><td>Cheikh Hassan</td><td>Early 11th mill. cal BP</td><td>Middle Euphrates</td><td>24</td></tr><tr><td>EPPNB</td><td>Dja'de III</td><td>Mid 11th mill. cal BP</td><td>Middle Euphrates</td><td>48</td></tr><tr><td>EPPNB</td><td>Cheikh Hassan</td><td>Mid 11th mill. cal BP</td><td>Middle Euphrates</td><td>5</td></tr><tr><td>EPPNB</td><td>Mureybet IVA</td><td>Mid 11th mill. cal BP</td><td>Middle Euphrates</td><td>10</td></tr><tr><td>MPPNB</td><td>Halula</td><td>10th mill. cal BP</td><td>Middle Euphrates</td><td>9</td></tr><tr><td>LPPNB</td><td>Halula</td><td>Late 10th mill. cal BP</td><td>Middle Euphrates</td><td>10</td></tr></tbody></table>

fruits such as Pistacia and wild cereals. This shift, combined with the expansion of open steppe landscapes, made natural cereal growth near the site unlikely and has been interpreted as possible evidence for early cultivation practices (Hillman, 2000). However, Colledge and Conolly (2010) proposed an alternative explanation, suggesting that changes in plant exploitation reflect a broader diversification of the diet in response to environmental stress. Similar climatic constraints at Mureybet during the late 13th and early 12th millennium BP would have made natural stands of wild barley and einkorn improbable near the settlement, implying that cereals were either gathered from distant wild populations or cultivated plots (Van Zeist and Bakker-Heeres, 1984; Willcox, 2008). Our functional data (Ibáñez et al., 2016, 2021) indicate that Natufian communities at Abu Hureyra and Mureybet harvested cereals at a

semi-green stage. This pattern is consistent with early management practices but does not exclude the possibility that people were exploiting wild, unmanaged stands where asynchronous ripening made such harvesting unavoidable. Experimental studies show that cutting semi-green cereals optimizes yields by limiting seed loss due to shattering, which could have encouraged the adoption of certain management strategies near settlements (Anderson, 1992; Willcox, 2007). Willcox (2024) suggested that Natufian groups may have observed accidental germination when harvested cereals were transported to habitations or storage areas and could have later mimicked this process by deliberately sowing cereals beyond their natural habitats, though such sporadic cultivation would likely leave minimal archaeobotanical traces.

At Mureybet III and Cheikh Hassan, cutting semi-green cereals dominates during the PPNA, with a similar trend observed at Jerf el Ahmar (Ibáñez et al., 2021). In the Early PPNB, semi-green cereals cutting remain prevalent at Dja'de III and Mureybet IVA, with over 50% of the samples falling into this category and start to decline at Cheikh Hassan. During this period, wild wheat (T. monococcum, T. dicoccum), barley (H. spontaneum) and pulses were the main food resource in the region (Colledge, 1998; Willcox et al., 2008; Willcox and Stordeur, 2012; Willcox, 2013; Arranz-Otaegui et al., 2016; Douché and Willcox, 2018). Warmer, wetter, and more stable conditions that marked the onset of the Holocene are supposed to have created the ecological basis for cultivation to emerge, and PPNA settlements show evidence of large-scale cereal processing (Colledge, 1998; Willcox, 2012). The introduction of founder crops at sites distant from natural stands of wild cereals, combined with the gradual increase in cereal grain size, a rise in potential arable weed taxa and a decline in the collection of edible seeds from non-founder plants in favour of founder crops have been interpreted as evidence of pre-domestication practices (Willcox, 2012). Recent re-evaluation of these assemblages however shows that these criteria are not unambiguous. Small proportions of 'domestic' spikelet bases can occur within wild populations, the identification of arable

<table><caption>**Table 7**

Classification of sample frequency according to the classification of the sample in each four groups. As not all the tools had the same quantity of surface samples, the results of the classification of the samples of each tool were pondered in base 10.</caption><thead><tr><th>Period</th><th>Sites</th><th>Ripe cereals</th><th>Semi-green cereals</th><th>Green cereals</th><th>Reeds</th></tr></thead><tbody><tr><td>Late Natufian</td><td>Mureybet/Abu Hureyra</td><td>10,51 %</td><td>55,86 %</td><td>29,45 %</td><td>4,18 %</td></tr><tr><td>PPNA</td><td>Mureybet III</td><td>22,67 %</td><td>34,00 %</td><td>27,76 %</td><td>15,58 %</td></tr><tr><td>PPNA</td><td>Cheikh Hassan</td><td>27,90 %</td><td>40,20 %</td><td>12,90 %</td><td>18,90 %</td></tr><tr><td>EPPNB</td><td>Dja'de III</td><td>28,30 %</td><td>61,64 %</td><td>7,78 %</td><td>2,27 %</td></tr><tr><td>EPPNB</td><td>Cheikh Hassan</td><td>52,00 %</td><td>38,80 %</td><td>4,80 %</td><td>4,40 %</td></tr><tr><td>EPPNB</td><td>Mureybet IVA</td><td>42,39 %</td><td>53,83 %</td><td>2,94 %</td><td>0,83 %</td></tr><tr><td>MPPNB</td><td>Halula</td><td>70,40 %</td><td>23,95 %</td><td>5,00 %</td><td>0,65 %</td></tr><tr><td>LPPNB</td><td>Halula</td><td>59,56 %</td><td>38,00 %</td><td>2,45 %</td><td>0,00 %</td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Classification of sample frequency according to the classification of the sample in each four groups.</figcaption></figure>
weed taxa is often uncertain, and for some cereals, such as wild rye, it
remains impossible to determine their area of natural grow and whether
they were cultivated or gathered and mixed strategies—light cultivation
supplemented with harvesting from wild stands—cannot be ruled out
(Douché and Willcox, 2023).

Across the region, before domestic cereals were dominant, the pro-
portion of samples classified as harvesting ripe cereals gradually
increased from 20 to 30 % at Mureybet III, Cheikh Hassan and Dja'de
during the PPNA, to over 40 % at Mureybet IV<sub>A</sub> and reaching 50 % at
Cheikh Hassan during the Early PPNB. This gradual rise in ripe cereal
harvesting from the Late 13th to the mid-11th millennium cal BP sug-
gests cultivation practices that promoted more synchronised maturation
(Anderson et al., 1991; Ibáñez et al., 2016, 2021; Pichon et al., 2021).
Such practices may have developed from the management of natural
stands to increasingly cultivation, involving varying degrees of soil
preparation. Following the PPNA, the first clear evidence of morpho-
logically domestic cereals—marked by non-shattering rachises (Fuller
2007; Willcox 2012; Zohary et al., 2012)—appears in significant pro-
portions (around 30 %) in the southern-central Levant (Arranz-Otaegui
et al., 2016; Douché and Willcox, 2018), while in Anatolia, early
non-brittle cereal rachises are recorded at Çafer Höyük (Tanno and
Willcox, 2012). In the Middle Euphrates, at Dja'de II and III, crops are
dominated by barley, rye, and emmer (Douché and Pichon, 2018;
Douché and Willcox, 2018). Although morphologically domesticated
cereals remain rare (ca 5 %), their robust grain morphology and the
presence of potential arable weed taxa have been interpreted as possible
indicators of cultivation, including tillage, in the Middle Euphrates
(Douché and Pichon, 2018; Douché and Willcox, 2018). Nevertheless,
recent ecological modelling, however, has proposed a different inter-
pretation for the plant communities associated with PPNA and Early
PPNB sites. According to Weide et al. (2021, 2022), the weed floras
identified at Jerf el Ahmar and Dja'de resemble untilled wild cereal
grasslands rather than cultivated arable fields, challenging the tradi-
tional interpretation arable weed as reliable indicators of early culti-
vation (Weide et al., 2021). These results suggest that pre-domestication
cereal exploitation relied on the natural competitiveness of wild cereals
in grassland environments, with limited mechanical disturbance and
without the need for regular tillage, due to the adaptive syndrome,
featuring large, awned seeds that bury easily and enable dense,
competitive stands in annual grasslands (Weide et al., 2022). In our
study, the dominance of semi-green harvesting at Cheikh Hassan and
neighbouring PPNA–Early PPNB sites aligns with our experimental
reference collection, which derives from tools used to cut semi-green
wild cereals grown under cultivation, based on annual sowing regimes
in fields that had undergone soil preparation (furrows or broadcast
sowing) and that grew alongside other wild grasses. Although the sce-
nario of cultivating wild cereals without systematic tillage proposed by
Weide et al. (2022) is not represented in our experimental harvesting
programme, the intensity of ground tillage at the sowing season should
not affect the characteristics of harvesting gloss texture—i.e., the in-
fluence of the amount of grit within the plant—on tools used during the
harvesting season (Anderson, 1992; contra Unger-Hamilton, 1985,
1992). By contrast, the quantity and characteristics of weeds in fields
that are artificially disturbed could have an impact on gloss texture.
Addressing this gap will require new experimental reference collections
specifically designed to document cereals cultivated under minimal but
regular human intervention.

Our results of reaping ripe morphologically wild cereals during the
PPNA and the Early PPNB may reflect several scenarios. Secondary
harvesting of mature stems for non-food purposes—such as roofing,
basketry, or fodder—could have taken place after primary harvesting of
semi-ripe cereals intended for grain acquisition. It could also indicate
that cultivated wild cereals were gradually evolving traits that favoured
riper harvesting prior to the fixation of the non-shattering rachis.
Repeated cycles of sowing and harvesting were likely to promote
synchronised maturation (Fuller, 2007), reducing the proportion of

immature grains (Fuller and Allaby, 2009) and thereby increasing yields
(Anderson, 1992). Consequently, cereals could have been harvested at a
more advanced stage of ripeness, favouring the development of larger,
fully formed grains, as seen at Jerf el-Ahmar and Dja'de III (Stordeur and
Willcox, 2009; Willcox, 2004; Douché and Willcox, 2018). These
"semi-domesticated" forms, though still retaining a brittle rachis,
exhibited domestication-related traits such as increased grain size and
the loss of dormancy (Fuller, 2007; Willcox, 2012). The model of cereal
cultivation under low-input exploitation practices (Weide et al., 2022)
does not exclude that repetitive cycles of sowing and harvesting could
have imposed selection pressures even in the absence of frequent soil
tillage, which could explain the progressive shift toward riper harvesting
of crops before the fixation of the non-shattering rachis.

By the Middle PPNB, at Tell Halula (mid-10th millennium cal BP),
reaping of ripe cereals becomes dominant regionally (around 70 %) and
remains prevalent into the Late PPNB, in the context of mixed farming
with domesticated emmer and barley alongside herding of pigs, sheep,
goats, and cattle (Willcox, 1996; Willcox et al., 2009). The expansion of
settlements and productive economies across the region reflects broader
agricultural intensification during this period (Goring-Morris and
Belfer-Cohen, 2008; Willcox, 2024). Although there is a gap in the
Middle Euphrates archaeobotanical record between the end of the Early
PPNB and the onset of the Middle PPNB (ca. 10,200–9800 cal BP),
non-shattering emmer and barley appear elsewhere in Southwest Asia
(Nesbitt, 2002; Tanno and Willcox, 2006, 2012; Asouti and Fuller, 2013;
Zeder, 2015). In central and southeast Anatolia, domesticated-type ce-
reals, including emmer, barley, and one-grained einkorn, are attested
around 10,300–10,200 cal BP (Tanno and Willcox, 2012). At Tell
Halula, two-rowed barley (Hordeum distichum), naked wheat (T. aesti-
vum/durum), and emmer (T. dicoccum) were the main cereals exploited,
while shattering types persisted sporadically, likely due to weedy forms
and gene flow from wild populations, which may have slowed fixation of
the non-shattering trait (Buxó and Rovira, 2013; Willcox, 2024).

In parallel, green cereal harvesting—experimentally linked to cut-
ting wild cereals from natural stands—declines from the PPNA levels,
becoming marginal thereafter, though it persists until the 10th millen-
nium cal BP. This likely reflects occasional collection of spontaneous
cereals to supplement cultivated seed stocks or manage risk. Diverse
gathering and harvesting strategies were possible: wild barley, still
locally available today, may have been managed or cultivated nearby,
while rye, less suited to today’s environmental conditions in the Middle
Euphrates area, may have been gathered from more distant regions
(Willcox and Stordeur, 2012), which has been the subject of renewed
discussion (Douché and Willcox, 2023). Harvests from wild stands could
also replenish seed reserves during crop failures (Willcox, 2024). In this
context, green cereal harvesting functioned as a flexible, short-term
strategy in response to environmental or socio-economic stress.

6. Conclusion

This study highlights methodological and analytical implications of
using high-resolution silicone moulds for texture analysis in use-wear
studies. By comparing direct measurements on flint surfaces with
those obtained from their corresponding moulds, we were able to
quantify the degree and direction of replication distortions and to
evaluate their impact on quantitative results. While silicone moulds
introduce systematic but predictable distortions to surface textures, they
remain a valuable tool for functional studies on archaeological artefacts
when direct analysis is not viable. However, combining data from
moulds and direct scans within the same discriminant analysis should be
avoided to ensure reliable interpretations. Overall, direct measurement
on original tool surfaces provides the most accurate results, while
moulding offers an important non-destructive alternative for studying
objects that cannot be moved from their storage location. From a
methodological point of view, the study also confirms that high-
resolution moulds can reliably capture functional wear
patterns—provided that both experimental and archaeological samples are analysed using the same protocol. While direct scanning remains preferable when feasible, the mould-based model proved robust and reproducible, enabling the integration of Cheikh Hassan into a broader regional comparative framework and contributing valuable data to ongoing discussions about the emergence of agriculture in Southwest Asia.

At Cheikh Hassan, use-wear analysis, based on high-resolution sili-
cone moulds reveals an increasing focus on semi-green cereal harvesting
at the transition between the PPNA and Early PPNB, alongside a gradual
decline in the use of green cereals. This trend parallels regional patterns
and suggests that the community participated in a broader shift towards
pre-domestication cultivation strategies. By the Early PPNB, the rise in
tools linked to ripe cereal harvesting indicates a further intensification
in crop management, supporting the idea that cultivation practices were
developing before full domestication traits appeared in plants. The
timing of cereal harvesting according to plant maturity likely had im-
plications beyond the act of reaping itself, influencing storage, pro-
cessing, and consumption practices within these early farming
communities (Anderson et al., 2015). Although this study does not
directly address these aspects, the results highlight how use-wear anal-
ysis provides a valuable perspective on the economic and social trans-
formations that accompanied the origins of agriculture, linking tool use
and agricultural development to the wider cultural and practical con-
texts of early farming societies.

CRediT authorship contribution statement

**Fiona Pichon:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Juan José Ibáñez Estevez:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Patricia Anderson:** Methodology, Investigation, Data curation, Conceptualization. **Frédéric Abbès:** Writing – review & editing, Validation, Project administration.

Declaration of generative AI in scientific writing

During the preparation of this work, the authors used ChatGPT (OpenAI) to assist with improving English language and phrasing. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.

Funding source

This work was supported by an Extraordinary grants 'from MSCA - IF to ERC', IFERC2022-España-CSIC (to F. Pichon, OBSTRADE project), the projects PID2022-142075NB-I00 and RED2022-134472-T, funded by MCIN/AEI/10.13039/501100011033 and by the European Union Next Generation EU/PRTR (to J.J. Ibáñez Estevez).

Declaration of competing interest

All authors declare that there are no conflicts of interest, competing interests, or any financial or personal relationships that could have influenced the work reported in this manuscript.

Acknowledgements

We acknowledge also the French Ministry for Europe and Foreign Affairs (MEAE) and the Directorate-General of Antiquities and Museums (DGAM) of Syria which supported the Permanent Mission of El-Kowm–Mureybet Mission at Cheikh Hassan and the Archaeological Mission of Dja’de el-Mughara. Additionally, we extend our gratitude to A. Teira-

Brión (University of Oxford, UK) and family for conducting cereals harvesting (*Secale cereale*) in Spain in 2022, funded by HORIZON Europe 2020 under the MSCA-IF project MILLET (H2020-MSCA-IF-2020, REA grant agreement ID: 101018935), and to A. Weide (University of Cambridge, UK) for harvesting wild cereals in Israel in 2022, supported by the John Fell Oxford University Press Research Fund. We also wish to acknowledge the Shelby White and Leon Levy Program for Archaeological Publications (Harvard University, USA), whose support through the projects “The Neolithic Site of Cheikh Hassan (Euphrates Valley, Syria)” and “Dja’de el-Mughara. A Ninth Millennium Sequence in the Euphrates Valley (Syria)” greatly contributed to the preservation and dissemination of primary archaeological data.

**Appendix A. Supplementary data**

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jas.2025.106446.

Data availability

The data supporting the findings of this study are available within
the article and its Supplementary Materials.

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