dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Marta V azquez Rodríguez , Ant onio Albuquerque and  Vasileios Kotinas  for kind help to collect seeds in  Furelos (Galicia, Spain), Bertiandos (Portugal), and Amitis (Naxos Island, Greece ), respectively; and  Agustín Merino  for support to access field sites in Amitis ( Naxos Island, Greece ) and  Arno (San Rossore, Italy). Portuguese Foundation for Science and Technology  funded this study through  ALNUS  project  (PTDC/ASP-SIL/ 28593/2017 );  Inês Marques  through a  PhD scholarship  ( SFRH/ BD/133162/2017 ),  Sofia I. R. Conceição  and  Ana D. Caperta  through grant  PTDC/AGRPRO/4285/BM/2014 ,  Patricia M. Rodríguez Gonz  alez through the  Investigador FCT Programme  ( IF/00059/2015 ) and the  CEEC Individual Programme  ( 2020. 03356. CEECIND) and  Forest Research Centre  through project  UIDB/00239/2020.
A joint measurement campaign took place using airborne and ground-based platforms  (Flamant et al. , 2017; Kalthoff et al. , 2018). The area of interest during this field experiment is indicated in Fig. 1 , which gives an example of LLSC horizontal extent between 05:00 and 07:00 UTC on 8 July 2016. One of the primary goals of this project was to provide the first high-quality and comprehensive dataset for a highly detailed study of LLSC. To this end, three so-called "supersites", which gather a large set of 10 complementary instruments, were installed at Kumasi (6. 68° N, 1. 56° E) in Ghana, Savè (8. 00° N, 2. 40° W) in Benin, and Ile-Ife (7. 55° N, 4. 56° W) in Nigeria (Fig. 1 ). The comprehensive dataset acquired at the Savè supersite paved the way for the first research studies of LLSC over SWA based on high temporal resolution observations.
The NLLJ is one of the main features of the West African monsoon season  (Parker et al. , 2005; Lothon et al. , 2008). The LLSC deck breakup after sunrise, which leads to a transition towards shallow convective clouds, has not yet been well documented with the unique DACCIWA dataset. Only  Pedruzo-Bagazgoitia et al. (2020)  have analyzed this transition by using idealized Large Eddy Simulations (LES), inspired by data collected during the LLSC occurrence on 25-26 June 2016 at Savè supersite. This was the first LES of the stratocumulus to shallow cumulus (Sc-Cu) transition over land in SWA. Our study analyzes the transition from LLSC to shallow convective clouds of twenty-two cases observed at the Savè supersite during the DACCIWA experiment. The results should provide complementary guidance for a numerical model evaluation of Sc-Cu transition over SWA.
Data and Methodology The period in which the DACCIWA field experiment took place (June-July 2016) was divided into four synoptic phases by  Knippertz et al. (2017) , based on the north-south precipitation difference between the coastal and Sudanian-Sahelian areas. The first phase, the pre-onset phase, ends on 16 June 2016 with a northward shift of rainfall maximum, indicating the settlement of the West African monsoon season  (Fitzpatrick et al. , 2015). The second synoptic phase, the post-onset phase, characterized by higher rainfall over the Sudanian-Sahelian area, lasted from 22 June to 20 July 2016. During the first days of this phase, namely from 27 June to 8 July 2016, undisturbed monsoon flow and an increase of low-level cloudiness were observed over SWA, especially over the DACCIWA investigated area.
We analyzed a set of twenty-two LLSC occurrences for which the cloud forms at night and persists at least until sunrise the next day. These cases have been selected over the period from 19 June to 31 July 2016 because of good data coverage  (Dione et al. , 2019). Only cases for which the stratus phase, determined by the methodology of  Adler et al. (2019) , started before 04:00 UTC on day-D+1 have been selected. Additionally, for each selected cases, no or only light precipitation (i. e. less than 1 mm) was recorded at the surface from 21:00 UTC on day-D to 16:00 UTC on day-D+1. Among these twenty-two cases, nine are IOPs, including the 07-08 July 2016 (IOP8) case  (Babić et al. , 2019a)  and the 25-26 June 2016 case (IOP3)  (Pedruzo-Bagazgoitia et al. , 2020). About 60% of the selected cases occurred between 26 June and 11 July 2016, a period which falls roughly within the three first weeks of post-onset phase, and is characterized by a low-troposphere dynamic typical for the West African monsoon season.
The BASE term is not estimated because the turbulent fluxes at LLSC base cannot be deduced from available dataset at the Savè supersite. According to Pedruzo-Bagazgoitia et al. (  2020 ), the BASE term is small at this time relative to the three terms RAD, ENT and SUBS. The latter are the most significant contributions in early morning that we attempt to estimate. (2) The RAD term (Eq. 1. d) is retrieved from the vertical profiles of upwelling and downwelling radiative fluxes which are computed using the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model  (Ricchiazzi et al. , 1998). This software tool, which solves the radiative transfer equation for a plane-parallel atmosphere in clear and cloudy conditions, was used in the studies of  Babić et al. (2019a)  and  Adler et al.
Therefore, the LLSC optical thickness is determined from a parameterized LWP (Eq. 2), by assuming an adiabatic cloudy layer in which the liquid water mixing ratio ( l ) increases linearly  (van der Dussen et al. , 2014; Pedruzo-Bagazgoitia et al. , 2020). The downwelling longwave radiations from potential mid-level and high-level clouds may reduce radiative cooling at the LLSC top (e. g. Christensen et al. , 2013). However, the cloud layers above the LLSC (base, top and water content) cannot be precisely described in the SBDART model from the available dataset. Thus, the radiative effect of higher clouds is not directly included in our estimate of downwelling radiative fluxes, but is partially taken into account through vertical profiles of temperature and relative humidity given by the radiosonde.
Despite the BASE term, the strong decrease of both ENT and RAD makes negative one hour after sunrise. The RAD and ENT terms cannot be estimated during the convective phase with the dataset acquired at Savè because several data are missing, among them the CTH. The C and DC scenarios during the convective phase are very close to the case simulated in Pedruzo-Bagazgoitia et al. (2020) and we can expect a quite similar evolution of terms involved in the LWP prognostic equation. Conversely, the DD scenario might be very different. The LLSC breaks up earlier, mostly before or around 10:30 UTC, when it is decoupled from the surface, likely due to a weaker BASE term. This hypothesis is supported by the findings of van der Dussen et al. (2014) suggesting that LLSC coupled to the surface moisture are more resistant to cloud-thinning related processes, such as the entrainment of dry and warm air into the cloudy layer.
Summary and conclusion The objective of this study is to examine the breakup of almost daily LLSC during the monsoon season in southern West Africa. It is based on the analysis of a set of twenty-two precipitation-free LLSC occurrences observed at the Savè supersite during the DACCIWA field experiment. The diurnal cycle of the LLSC consists of four main stages and this study addresses the last two, the stratus and convective phases. We used the ground-based observational data collected by (i) ceilometer and cloud radar for the cloud layer macrophysical properties, (ii) energy balance and weather stations for atmospheric conditions near the surface, and finally, (iii) radiosoundings and UHF wind profiler for thermodynamical and dynamical conditions within the low-troposphere. From these measurements, some diagnostics of the LLSC layer are estimated, including: cloud base height, cloud coverage fraction, cloud base homogeneity and cloud layer coupling with the surface.
However, the C cases occurred preferentially between 27 June and 8 July 2016, a period with a well-established monsoon flow over West Africa, especially over the DACCIWA investigated area. Most of the D cases are observed during the monsoon-onset period or during disturbed sub-periods after 08 July 2016. If the synoptic conditions of monsoon flow play a role in the LLSC coupling with the surface, it could be through thermodynamical conditions, which were only slightly apparent in the Savè dataset. It could also be through large-scale dynamical parameters like large-scale subsidence, which is an important factor in the LWP single improvement in the models. The aerosol loading in the low-troposphere is a potential factor in controlling LLSC evolution and lifetime  (Deetz et al. , 2018; Mohrmann et al.
DEXi Methodology DEXi is software that can simplify complex systems when making multi-attribute decisions (the latest version, DEXi 5. 04, is available online  [42] ). It is specially designed for choosing among multiple options, scenarios, or systems by considering many parameters  [28]. Briefly, it breaks down a multi-factorial problem into smaller sub-problems until it obtains several problems that are easier to solve. DEXi can then be used to evaluate different scenarios or options for a multi-factorial system and help make decisions. Designing a DEXi model requires multiple steps (Figure  1 )  [31] :.
Then, a second online survey was sent to partners that asked them to weigh the criteria for each level of the tree. Mean values were used when the weights of participants converged. When they diverged, they were discussed and validated in the last workshop. During this workshop, the thresholds within the scale allocated to each indicator were also defined. Finally, a complete tree was obtained, thus dividing overall sustainability into sub-attributes that continued until they reached measurable indicators. The scales of these indicators were automatically defined according to the thresholds previously determined and recorded in the DEXi software. discussions. They then developed indicators that represented each sub-attribute. The indicators needed to (i) be associated with available data, (ii) define scales and thresholds, and (iii) be able to distinguish differences among systems/scenarios.
In addition, three rules were followed: to have few indicators for each sub-attribute, sufficient comprehensiveness, and no redundancy in indicators of the sub-attributes. The proposals from each workgroup were compiled to obtain a definitive version of the attribute tree. Then, a second online survey was sent to partners that asked them to weigh the criteria for each level of the tree. Mean values were used when the weights of participants converged. When they diverged, they were discussed and validated in the last workshop. During this workshop, the thresholds within the scale allocated to each indicator were also defined. Finally, a complete tree was obtained, thus dividing overall sustainability into sub-attributes that continued until they reached measurable indicators. The scales of these indicators were automatically defined according to the thresholds previously determined and recorded in the DEXi software.
• Collect data for the DEXiAqua tree, including those needed for LCA, emergy analysis, LCC, and SLCA (Figure  3 ); The template could not calculate all of the indicators itself. LCA and emergy indicators were calculated using other tools based on the data set collected, and then their results were incorporated into the template to determine the DEXi scales. The template could not calculate all of the indicators itself. LCA and emergy indicators were calculated using other tools based on the data set collected, and then their results were incorporated into the template to determine the DEXi scales. The template included the following: -Sheets to fill out: One template must be filled for each system assessed in order to be able to compare sustainability among systems by exporting lists of scales into DEXi software.
Description of the Reference Case Study as a Synthetic Scenario A reference case study was evaluated using DEXiAqua. This assessment involved applying the material developed to a data set, i. e. , the comprehensiveness of the template and its ability to sort a formatted list of scales into input for the DEXi software. It assessed whether the DEXi model provides results that are understandable, consistent, and explainable based on the characteristics of a production system. The case study was based on a French fish farm that produced 56 tons of Atlantic salmon per year in a land-based recirculating aquaculture system. The water loop includes a mechanic filter extracting the suspended solids, stocked and sent for crop fertilization; a biologic filter for ammonia nitrification; and an ozonation device for disinfection and oxygenation of the water.
Initial Tests of the Sensitivity of DEXiAqua To perform an initial sensitivity analysis, a Python script was used to generate randomized systems (Supplementary File S1). These random systems were assessed using DEXiAqua with DEXiEval, which consists of command lines written in the Windows Services console (available at: https://kt. ijs. si/MarkoBohanec/dexieval. html, accessed on 14 January 2020). Another Python script was used to export results to Microsoft ® Excel (Supplementary File S1). Seven simulations were tested: -"Random": a completely randomized simulation in which the value of the scale of each attribute was randomly generated; -"Eco_Low", "Env_Low", and "Soc_Low", in which all attributes of the given dimension received the worst score, while the values of the remaining attributes were randomly generated; -"Soc_Eco_Low", "Soc_Env_Low", and "Eco_Env_Low", in which all attributes of the two given dimensions received the worst score, while the values of the remaining attributes were randomly generated.
In our study, the reuse of material and energy is mainly considered at the farm level, where the management decisions of the farmer are applied. We considered the origin of the inputs and their potential recycled sources. Nevertheless, the fate of co-products downstream the farm, and their potential recycling by other actors in the territories, are poorly included. This is a way of improvement in future versions of the assessment method. DEXiAqua was tested on a monoculture system that had been studied in previous projects. Thus, an important parameter was not considered: the time needed to collect data. Data collection was difficult, even for a simple system, due to the large number of indicators required to reflect overall sustainability. Data collection will likely be more complicated and time-consuming for new systems, especially multitrophic systems.
This pitfall was partly expected because the model was designed according to project partners who knew well what kind of data should be collected. It was also expected due to the data-collection template's inclusion of calculation sheets to simplify collection. However, the attributes used depend on other methods whose results cannot be calculated automatically. For example, the template helps collect data to calculate LCA indicators, but it cannot actually calculate them. Doing so requires the intervention of an expert. One remaining factor was not expected and will be a future focus of the project. The SIMTAP project will design and construct prototypes from which data can be collected. These are not operational systems. Comparing IMTA systems to traditional systems requires upscaling the former to convert prototype data into a potential operational system.
Theoretical Implications This article describes a robust method to assess the sustainability of aquaculture systems with special emphasis on IMTA systems. It is based on combining parameters, including LCA, LCC, SLCA, and emergy accounting results, using DEXi freeware. A template was designed to help users easily provide data to perform the assessment. The template automatically transforms data into scaled indicators ready to be used by DEXiAqua. The method was developed by an expert consortium after multiple workshops and surveys that resulted in a consensual framework. It guarantees that, from the SIMTAP expert's perspective, the attributes considered are the most relevant to assess the sustainability of aquaculture systems. However, to prevent possible divergences between experts linked to specific systems, this framework can be adapted to the geographic, economic, social, and environmental contexts of specific case studies.
Acknowledgments: We thank the partners of the  SIMTAP  project who helped develop DEXiAqua:  Alberto Pardossi ,  Juan Francisco Fierro Sañudo ,  Lorenzo Rossi ,  Chingoileima Maibam  ( University of Pisa, Italy, Department of Agriculture, Food and Environment ),  Carlo Bibbiani ,  Baldassare Fronte  ( University of Pisa, Italy, Department of Veterinary Sciences ),  Daniele Torregiani ,  Alberto Barbaresi ,  Enrica Santolini  ( University of Bologna, Italy ),  Rainer Linke  ( Kolorev GmbH, Bonn, Germany ),  Kyle Spiteri  ( Ministry for Agriculture, Fisheries, and Animal Rights, Marsa, Malta ), and  Mehmet Ali Turan Koçer ,  Hüseyin Sevgili  ( Mediterranean Fisheries Research Production and Training Institute, Antalya, Turkey ). Finally, the authors thank  Tristan Senga-Kiesse  ( INRAE ) for good advice about sensitivity analysis and  Thomas Foricher  for help writing Python scripts.
Former field systems (FFS) are the most widespread traces of ancient activities in present European landscapes and represent significant perturbations to ecosystems. Through its ability to penetrate forest canopies and detect microlandforms, Airborne Laser Scanning data reveal archaeological relics over large areas, from periods older than the first available Historical Topographic Maps. Mapping these traces from ALS-derived data (e. g. Digital Elevation Model (DEM)) thus allows for a determination of a new temporal baseline in order to evaluate the effects of a longer history on current patterns of biodiversity. Here, we evaluate the ability of traditional machine learning (Random Forest-RF) and deep learning (Fully Connected Networks-FCN) models to detect Medieval Terraced slopes and Ridges and Furrows (RaF) from an ALS-derived DEM in the southern Vosges (1462 km 2 ).
We used a combination of Local Binary Patterns and topographical metrics to measure properties of FFS and to train detection models. We then assessed the relative performance of each model semantically and spatially. Our results demonstrated the high suitability of our approach for reproducing major trends in the landscape with a high level of similarity between the predicted and reference spatial patterns (Structural Similarity Index -SSIM > 0. 75). RF outperformed FCN for Terraced Slopes, whilst minimizing the false positive rate. FCN slightly outperformed RF for the RaF dataset but showed promising abilities to survey unseen data with a low sensitivity to annotation errors. We suggest that this approach has the potential to offer new spatio-temporal possibilities in Historical Ecology studies as a means of automatically detecting past archaeological ecosystems from a landscape to a regional scale.
, 2019; Trier et al. , 2019). As computational resources have greatly improved over the last decade, this approach has been highly successful due to its higher predictive performances and diminishing processing times. Despite these recent advances, there are still uncertainties regarding the optimal methods for analysing ALS data at a large scale. This uncertainty is a major limitation regarding the use of ALS data within landscape ecology studies. First, DL is often favoured over traditional ML, although questions remain concerning the superiority of DL when using ALS-derived elevation data, most often contained in single-channel images. Second, earlier studies used to systematically employ DL models, such as Convolutional Neural Networks (CNNs), but the quality of the results proved to be spatially uneven depending on the surveyed landform  (Verschoofvan and Lambers, 2019; Lambers et al.
, 2019; Zingman et al. , 2016; Gallwey et al. , 2019). Despite these advances, there are several major limitations for these models beyond the commonly cited 'black box' issue  (Verschoof-van and Lambers, 2019). First, DL models (including CNNs) are particularly sensitive to unbalanced data sets, a well-known issue in archaeological training sets. To overcome these limitations, researchers use data augmentation and apply transformations, such as flip or rotation in predefined directions  (Trier et al. , 2019; Verschoof-van and Lambers, 2019). Nonetheless, the results are mixed. A possible explanation is the difficulty in reproducing the finer-scale variations of landforms, or the frequently changing luminosity observed in data acquired under uncontrolled flying conditions, as is often the case with ALS data.
The rebalancing of the data set is therefore ineffective and classification outputs are not satisfactory, namely for the under-represented class in the learning set. Moreover, questions also remain in terms of the initialisation process, because of the need to estimate a high number of parameters. Studies have shown that existing pretrained models, such as ResNet or VGG-16  (He et al. , 2016) , are less prone to overfitting and can generalise fairly well compared to models trained on smaller data sets. However, these models have been trained with three-channel RGB images, not with single-channel images, making the transfer learning with ALS data challenging. A recent deep CNN, trained using Lunar LiDAR data sets (a large scale digital DEM dataset acquired by the Lunar Reconnaissance Orbiter  (Zuber et al.
Data We used ALS data obtained from the French Mapping Agency (RGE ALTI) under their Open Government License. The ALS data for the southern part of the Vosges Mountains were acquired in January 2013 and comprise 1 km by 1 km tiles of point cloud data (. las file v. 1. 2 compressed to laz). Each point was categorised into one of eight classes, including "ground", "building", and "vegetation". The average ground point density per square meter was 5, although this varied strongly (1-7) among the tiles, depending on vegetation density and the presence of buildings. This dataset was available as Digital Elevation Model (DEM),  From previous fieldwork and the visual inspection of the interpolated ALS data, we manually digitised every visible FFS on the entire study region, namely 305 TS and 112 RaF objects.
Then, this dataset has been used as training and validation sets in the development of detection models, for the present study (see Section 2. 5). The slopes of the TS varied from 6. 7 • to 30. 9 • (mean = 18. 6 • ± 4. 7 • ), and TS were found equally on southern and northern exposures (respectively 164 on southfacing and 142 on north-facing slopes). We also observed, however, a linear relationship between elevation and TS exposure; as TS elevation increased, the proportion of TS on north-facing slopes decreased (only 30% of these TS were found above 700 m). TS were covered mostly by grasslands (156/306 sites) and forests (130/306 sites). RaF were found mainly in the southern part of the study site and were markedly less common than TS (Fig. 1 ). They were observed mostly in relatively flat areas (average slope = 4.
The second step eliminated "negative" candidates from the learning set to reduce the class imbalance caused by an under-representation of positive examples relative to negative examples. Background examples were also over-represented for multiple reasons  (Trier et al. , 2019). First, at the regional scale, the background is the most common situation in the landscape. Second, an archaeological object is, by nature, rare, thereby limiting the number of occurrences in a learning set. Therefore, the developed models tend to overdetect negative examples at the expense of the positive ones; the latter includes a significant part of the landscape characteristics. We performed three cleaning steps. (1) We used the OpenStreetMap landcover database to eliminate negative candidates crossed by linear objects, such as roads or pedestrian pathways.