dataesr/scientific-paragraphs-categorization huggingface dataset:
This research was carried out within the framework of the  SIMoS  Project supported by the  Agence Nationale de la Recherche  from the  « Investissement d'Avenir » program   ANR-11-LABX-0032-01 LabEx   ARCHIMEDE  and the  Baboons Project  supported by the  Ministère de la Culture et de la Communication. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Moreover, it does not consider the fact that the demand for that chateau may have evolved differently from the market as a whole between years V -1 and V. 2  In this paper, we exploit a dataset containing individual wine prices on both the primary and secondary markets. We maintain the assumption that prices on the primary market must be consistent with those in the secondary market. We first model the prices on the secondary market just before the beginning of the en primeur campaign (end of April). This gives us a precise picture of how the market values each chateau. We then exploit this information to infer an appropriate release price for the new vintage. Our approach is similar to the one used in the bond market. When an issuer wants to determine the coupon to be offered on new bond issuances in the primary market, it will check the yield required by investors for the bonds already trading in the secondary market.
However, this parsimony also leads to limitations. The biggest drawback of this approach is that it does not consider that some wines may evolve differently from the index. For the present study, we take advantage of a unique dataset from the Bordeaux wine trade. It contains prices on the secondary market for individual chateau × vintage pairs. This allows us to directly model the prices of the various crus (and not only their variations from one vintage to another). We will present our dataset in the next section.
III. Data A. Prices on the primary and secondary markets Our dataset consists of primary and secondary market price data for 74 red Bordeaux wines. They represent the majority of the Bordeaux wine market activity. We exclude white and sweet wines because they embody a tiny share of the wines traded and would add unnecessary noise to the different specifications. For the primary market, we collected the ex-negociant en primeur prices of the 2021 vintage as well as the three previous ones (2018 to 2020). This data comes from Liv-ex. For the secondary market, we obtained prices directly from negociants at La Place (negoce prices) just before the beginning of the 2021 en primeur campaign, that is, at the end of April 2022. The data contains prices for the various wines from the 2005 to 2020 vintages.
Therefore, our database contains 1,177 prices from the secondary market (vintages 2005 to 2020) and 296 en primeur prices (vintages 2018 to 2021). Since the secondary market data is from 2022, we focus on modeling the prices of the 2021 vintage offered en primeur in 2022. For additional analyses, en primeur price data from the 2018 to 2020 vintages are used. Table  1  presents price statistics by vintage. At first glance, the en primeur (primary market) prices for 2021 wines appear reasonable. On average, they are significantly lower than the current secondary market prices of the last three vintages. However, 2018, 2019, and 2020 are widely considered great vintages, and other older vintages have average prices close to and sometimes even lower than the 2021 vintage. For example, the 2014 vintage, which has been frequently compared to the 2021 vintage (wet and not very hot summer, wines with a "classic claret" aromatic profile), has a similar average price level, even though these wines are bottled and seven years older.
This is becoming increasingly the case with global warming (see  Ashenfelter and Storchmann (2016)  for a review of the impact of weather and climate change on wine). However, more granular weather-related data retrieved from satellites appears to somewhat alleviate these concerns and difficulties and may constitute an interesting approach in the future. Therefore, we opt for vintage charts rather than weather-related data. We compile and aggregate data from nine experts/reference publications. Aggregation reduces subjectivity bias and helps achieve an objective score  (Cardebat, Figuet, and Paroissien, 2014). We include international (The Wine Advocate, Wine Spectator, Wine Enthusiast, Vinous, Decanter, Wine Cellar Insider) and French (Revue des Vins de France, Guide Hachette, Idealwine) experts.
Some international references (Jancis Robinson, James Suckling, and Jean-Marc Quarin) could not be considered because they do not provide an aggregate and numerical evaluation of the various Notes: Prices correspond to the current prices on the secondary market for vintages 2005 to 2020 and the prices on the primary market for vintage 2021. Vintage evaluation is the aggregate vintage rating (based on data from nine individual vintage charts from international wine experts/publications). TWA and GWS denote information on ratings from The Wine Advocate (TWA) and Global Wine Scores (GWS). For vintage 2021, GWS scores are unavailable and have been replaced by an aggregate score computed using a conceptually similar methodology. vintages. In order to control for differences in the rating scale and the way it is used (some experts are more generous and/or make more significant differences between the best and worst vintages), we standardize all the evaluations by subtracting the average and dividing by the standard deviation.
C. Market reaction to the release of en primeur wines We obtain price data from Liv-ex for Bordeaux 2021s, for which trading activity on their platform exists. In comparison to the overall en primeur Bordeaux market, liquidity is limited. However, the prices recorded on Liv-ex represent actual transaction prices on the secondary market. This allows us to understand how the market reacts and how the various wine prices evolve just after their release. A total of 15 wines from our sample have been traded on Liv-ex. In unreported results, we find that prices have deviated on average by -4% (median: -10%) from their release prices. Nine wine prices decreased on average by -14%. Six wine prices increased on average by 11%, but this high average is mostly due to two wines that performed very well: Carruades de Lafite (+37%) and Carmes Haut-Brion (+15%).
V. Conclusions This paper uses the Bordeaux wine market to examine the fixation of efficient release prices. Based on the fact that the primary and secondary markets are necessarily linked, we propose a model for estimating the price of wines on the secondary market, which we then apply to the wines released on the en primeur market. Our model includes brand value, vintage quality, ratings, and age variables. Compared to past studies on the pricing of en primeur Bordeaux wines, our model has a high explanatory power of 98% and helps to determine prices (rather than returns) for individual wines. This is due to the availability and use of more fine-grained secondary market price data for individual wines and not only an aggregate benchmark such as the Liv-ex 100. We document that the 2021 vintage was overall too expensive and that many producers have not adjusted their prices downward enough to counter a vintage that was only of good quality.
For consumers, collectors, or investors, it allows obtaining efficient prices against which actual issue prices can be compared. This helps to avoid purchasing wines at inflated prices, which may hurt subsequent returns or make it worthwhile to buy physically available wines. Hedonic pricing models have reached a certain maturity in academia (see  Oczkowski and Doucouliagos (2015)  for a meta-analysis) to characterize wine prices in multiple regions. Models to estimate efficient release prices, particularly in Bordeaux, have also gained some traction in recent years and will continue to be important as the wine market develops. As in traditional financial markets, the modeling will evolve and increasingly rely on novel estimation techniques and larger datasets in terms of prices, expert scores, and increased liquidity on the secondary market.
The ongoing development and increased usage of, for example, Liv-ex for secondary market data or the wine community for quality cues  (Dubois, 2021; Oczkowski and Pawsey, 2019) , will allow for larger datasets and ultimately favor big data approaches. Moreover, machine-learning techniques have started to be used to estimate wine quality based on attributes such as alcohol, sulfites, or acidity  (Gupta, 2018; Koranga et al. , 2021). These methods may ultimately be extended to new data to provide more accurate price or expert score estimates. However, the central question of the persistence of market inefficiency for young wines raised by  Ashenfelter, Ashmore, and Lalonde (1995)  remains open. This needs to be put into perspective. The dominance of Bordeaux fine wines has diminished over the past 20 years.
Geoarchaeological inventory and selection process of geoarchaeosites A preliminary inventory process has been conducted at the scale of the Middle Eure Valley defined as the reference space. This inventory is based on the data collection from the Patriarche and Mérimée databases of the French Ministry of Culture and from the archaeological maps of Gaul, completed by the resources from the National Inventory of Geological Heritage and from geological and geomorphological maps of the Eure (27), , and Yvelines (78) departments (Table  2 ). A second stage involved the identification of potential geoarchaeosites by merging archaeological and geo(morpho)logical values along three axes: (1) spatial, (2) temporal, and (3) spatio-temporal (Fig. 3 ; Table  3 ). The criteria defining the archaeological and geoscientific values are based on large-scale studies conducted in the past 30 years on settlement dynamics and spatial organization of territories in the long run  (Favory & Van der Leeuw, 1998) : (1) The spatial axis selects entities representing (i) archaeological diversity of human settlement (archaeodiversity) and (ii) geodiversity; (2) The temporal axis selects entities enabling the reconstruction of the successive steps of (i) human settlement and (ii) morphogenesis; (3) The spatio-temporal axis considers (i) the diversity of human settlement patterns within the geomorphological system and (ii) the diversity of human settlement in terms of permanence and continuity.
Participants Starting in 2011, we used a paper version of the questionnaire, and collected data from a first set of participants. In 2017, we implemented an online version of the questionnaire, which allowed us to collect a larger set of data. Data were collected in total from 99 control participants (paper version, n = 11; online version, n = 88) and 41 amusic participants  (paper version, n = 6; online version, n = 35). Participating in this questionnaire study was proposed to volunteers that came to the lab to participate in behavioral or neuroimaging studies investigating congenital amusia, and thus underwent a full assessment allowing diagnosis of congenital amusia (audiometry, MBEA, PDT, see below for details). To match amusic participants, nonmusician control participants were recruited (i.
Subjective Experience of Verbal Earworms Control participants who reported having verbal earworms reported these earworms as a neutral (n = 14 [47%]) or a negative (n = 13 [43%]) experience, and only rarely a positive experience (n = 3 [10%], missing data for one participant). Amusic participants reported equally verbal earworms as a negative (n = 8 [36%]), a neutral (n = 7 [32%]), or a positive (n = 7 [32%]) experience (the proportion of the three responses differed significantly between groups, Fisher's Exact test, p = 0. 01). When asked how the verbal earworm terminates, control participants mostly reported that it goes away by itself (n = 23 [77%]), yet a few deploy a voluntary strategy to terminate it (n = 4 [13%]) or report that it is almost impossible to get rid of it (n = 3 [10%], missing data for one participant).
Almost all amusic participants reported that it goes away by itself (n = 19 [90%]), a few deploy a voluntary strategy to terminate it (n = 2 [10%]), none reported that it is almost impossible to get rid of it (missing data for one participant). The proportion of the three responses differed significantly between groups, Fisher's Exact test, p= 0. 04. Among the six participants who reported using a voluntary strategy to get rid of the verbal earworm, two controls reported changing activity (e. g. , switching on the TV), two amusics to think about something else (e. g. , to-do list), and two controls to reflect on the repeating words (described as ruminations).
Regarding individual differences of INMI, Liikkanen and Jakubowski (2020) review that only a weak negative relationship was observed between age and INMI frequency, with data suggesting a mediating factor of musical practice. In our study, mostly non-musicians were tested, confirming a significant negative relationship (see also  Liikkanen, 2012) ; the older they were, the weaker the reported frequency of occurrence of INMI. We also assessed the link between INMI and long-term memory capacity, notably by taking advantage that all participants had been tested with the MBEA (to ascertain absence of amusia). In particular, we used the MBEA subcomponent testing for incidental memory to investigate the potential link between INMI and long-term memory capacity. Across all participants, the incidental memory score of the MBEA correlated significantly with musical earworm occurrence, further supporting the link between INMI and memory (e.
Methodology This research plan will use a hybrid methodology that combines positivism and interpretivism, supplemented by pragmatism, to provide answers to the problems above. During the search and data-gathering process, many methodologies will used to examine the style phenomenon in fashion photography. The project will begin with an initial survey to gather data via interviews and observations conducted with industry experts, including photographers, fashion designers, creative directors, and makeup artists. Subsequently, the researcher will create a tool using the initial study data and collect information via focus group talks to ascertain art direction, style components, and trends. The findings of this research will provide aspiring photographers with novel data that may be analysed or used.
Quantitative techniques will used to capture and organise visual data during fashion photography research on the concept of the style process. This methodology will used to research humans in their natural environment through observations and interviews. Data will collected by professional photographers, art directors, and stylists. This study will use a variety of methodologies from different fields of study. It will specifically concentrate on enhancing fashion photography in order to get the necessary data for research via the process of data analysis and recording. Qualitative research necessitates data, and the study of documents and resources. By utilising data analysis and photographic recording, we may enhance our comprehension of enrichment processes, enabling us to discern and categorise them visually.
Acknowledgement Thank  Allah Subhanahu Wa Ta 'ala for his blessings, which allowed me to conclude my report. Throughout this process, numerous individuals have assisted me. I express our profound gratitude to the  College of Creative Arts Studies at Universiti Teknologi MARA (UiTM) Malacca Campus, Malaysia. Dr Nadzri Mohd Sharif  led me through this research correctly. A strong desire to explain things quickly helps me understand; this keeps me driven and helps me develop new ideas. In addition, I want to thank my family, particularly my wife and children, for their love and inspiration throughout the years. They made it possible for me to complete my research. Finally, thank the  Department of Visual Arts, Universiti Selangor , personnel for their assistance.
Acknowledgments: The author would like to thank all the students who contributed to the gel studies, and especially  Andrea Dupke ,  Alban Gomez ,  Tiffanie Giraud ,  Clémence Fontaine ,  Erwan Guibert-Martin ,  Sotiris Bampitzaris  and  Eve Paillaux. The author wishes to express her gratitude to the conservators, and more particularly to  Loretta Rossetti  and  Aymeric Raimon , as well as to  Charlène Pelé-Meziani  for the analyses. The author really appreciates everything the co-founders of the gel metals project,  Manuel Leroux  and  Aymeric Raimon , have done. Many thanks to  Edith Joseph  for having associated the author to the  Helix  project and to  Luana Cuvillier  for her high-quality work in this project.
e. CpG and TpG). The latter two were selected since methylated cytosines that are deaminated postmortem at CpG sites are sequenced as TpGs following USER enzymatic treatment, while those left undamaged are sequenced as CpGs. 24 r assay retrieved good sequence coverage across all targeted CpG sites (17. 20-to 195. 34-fold) for a total of 33 ancient horse remains from France and spanning the last $2,300 years. The sequence data proved sufficient to infer DNA methylation at 2,171 CpG sites, due to their physical proximity to the 1,611 CpG sites originally targeted. Repeating the methodology described above on the captured sequence data, all the findings could be recapitulated, excepting that the correlation between the modern and the transformed ancient DNA methylation values was stronger than when using WGS data (Spearman correlation coefficient = 0.
Sex determination and castration Males and females exhibit different DNA methylation patterns on the X chromosome,  40  which provides an additional opportunity to validate the mathematically transformed DNA methylation values inferred for the ancient horses. We used WGS data and X-to-autosomal coverage ratios to determine their genetic sex, following the study by Schubert et al. 41  Principal component analysis of DNA methylation at the 960 CpG sites located on the X chromosome present in our modern comparative panel revealed clear clustering of 42 ancient males and 8 ancient females with modern animals of the same sex. Two of the 333 modern horses that were previously reported as males  36  were, however, found to group with females, likely indicating erroneous sex assignment in the original data repository (Figure  5A ).
We caution that the data available for training should be extended in a larger number of male individuals, especially in castrated males, before the exact performance of each methodology can be fully appreciated. Both RF and GKNN returned substantial classification errors, and average assignment probabilities below 0. 25 and 0. 425, respectively (Figure  5F ). In contrast, SVM only returned the expected predictions, with high average assignment probabilities to both categories (0. 93 for intact stallions, and 0. 96 for castrated males; Figure  5F ). Therefore, we used the SVM approach to assess the castration status of 8 ancient horses aged above 11 and showing sufficient WGS data. None of these specimens were predicted to be castrated, including three Pazyryk horses from Berel', Kazkhstan, a 2,300-year-old funerary ritual site known for the sacrifice of males.
Read processing, trimming and alignment Sequencing reads were processed as described by Librado and colleagues. 11 Briefly, fastQ sequencing reads were demultiplexed, collapsed and trimmed using AdapterRemoval2 (version 2. 3. 0; -barcodemm-r [12] 1 -minlength 25 -trimns -trimqualities -minadapteroverlap 3 -mm 5). 47 In PALEOMIX version 1. 2. 13. 2,  48 collapsed, truncated and paired reads were aligned against the horse reference genome EquCab3,  60  supplemented with the Y-chromosomal contigs from  Felkel et al. 2019. 61  This was achieved using Bowtie2  49  and the parameters from. 62 BAM alignments were realigned locally around indels. Reads showing mapping quality below 25 and PCR duplicates were filtered out. Post-mortem DNA damage levels were measured on 100,000 random reads per library using the mapDamage2 software (version 2.
Ancient DNA methylation inference The normalized methylation data for modern horses based on the HorvathMammalMethylChip40 array  36  were downloaded from Gene Expression Omnibus (Accession Number GSE174767). The ancient genomes used in the present study are sequenced from tooth or bone DNA extracts. As no reference methylation maps for modern horses are available for these specific tissues, we used averaged values over all tissues, based on the assumption that bone and tooth methylation levels are similar to mean methylation levels across tissues. 63 Spearman correlation levels between untransformed ancient and modern DNA methylation levels for different average cellular methylation fractions (70%-95%), and CpG window sizes (from 1 to 50 CpGs), are shown in Table  S2A.
Mathematical transformation of ancient DNA methylation values The major obstacles to correlating modern and ancient DNA methylation levels pertain to the biases introduced by the different detection methods used (DNA sequencing for the former and DNA methylation array for the latter), but also to the lack of fine-scale resolution of ancient methylation maps. 64 To improve ancient DNA methylation inference, we applied mathematical transformation of DamMet methylation scores based on a two-step procedure. This first step built on a previously-described machine learning approach,  65  which we extended to output DNA methylation percentage values instead of binary 0 and 1 categories. To achieve this, we used the randomforest (RF) package 66 in R4. 1  67  and regressed the DNA methylation values inferred by DamMet 26 from ancient DNA sequence data against those measured in modern horses, accounting for the sequencing depth of the ancient data for each CpG site.
We built separate RF models for ancient WGS and capture data using the same procedures. For modelling, we excluded age-dependent CpG sites as well as those diagnostic for castration, leading to input datasets of 29,655 sites for WGS and 1,902 sites for capture data. Input data were randomly split into a training set (90% of the samples) and a test set (10%) for validation. The best X parameter identified and Equation  1 were the used to transform DNA methylation values for the 31,836 sites considered for WGS data, or the 2,171 sites considered for DNA capture. As a further transformation step, we conducted a Simple Linear Regression (SLR),  37  following the formula below, where Predict(RF) represents the predicted value obtained from the model optimized following RF and described in 1: modF = a 3 predictðRFÞ + b (Equation  2 ) Sites with residual errors above one standard error were excluded for later analysis.
Principal component analysis Principal component analysis (PCA) of DNA methylation values was performed using the R function prcomp,  46  on the merged dataset of samples passing the filtering criteria (333 modern individuals and 84 ancient individuals, Figure  S2B ). DNA methylation values for modern individuals corresponded to those measurements returned by the HorvathMammalMethylChip40 array,  36  while those of ancient specimens were the values inferred by DamMet,  26  following the transformation procedures described above. We further attempted to classify castrated males and intact stallions based on DNA methylation values at these CpG sites. For this, we explored the performance of three machine learning methods: Random Forest (RF), Support Vector Machine (SVM) and Generalized k-Nearest Neighbors (GKNN) (Figure  3 ).