dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Sets and Descriptors Four datasets for the following types of reactions were used in this study: bimolecular nucleophilic substitution (SN2)  [16] , bimolecular elimination (E2)  [21] , Diels-Alder reactions (DA)  [22]  and tautomerization (Tautomerization)  [23]. These datasets were collected in our previous studies. Chemical transformations were encoded by Condensed Graphs of Reaction (CGRs) (Figure  1 )  [29, 31]. ISIDA fragment descriptors were generated for the CGRs using the ISIDA Fragmentor  [30]  program. ISIDA fragment descriptors represent subgraphs of different topologies and sizes. Each subgraph is considered as a descriptor, whereas its occurrence in a molecule is the descriptor's value. In this study, sequences of atoms and bonds (subgraphs) were considered. The number of atoms in a Building and validation of AD definition models.
Data Sets and Descriptors Four datasets for the following types of reactions were used in this study: bimolecular nucleophilic substitution (S N 2)  [16] , bimolecular elimination (E2)  [21] , Diels-Alder reactions (DA)  [22]  and tautomerization (Tautomerization)  [23]. These datasets were collected in our previous studies. Chemical transformations were encoded by Condensed Graphs of Reaction (CGRs) (Figure  1 )  [29, 31]. ISIDA fragment descriptors were generated for the CGRs using the ISIDA Fragmentor  [30]  program. ISIDA fragment descriptors represent subgraphs of different topologies and sizes. Each subgraph is considered as a descriptor, whereas its occurrence in a molecule is the descriptor's value. In this study, sequences of atoms and bonds (subgraphs) were considered. The number of atoms in a fragment varies from 2 to 4, which was often one of the best fragmentation schemes in our previous studies  [16, [21] [22] [23].
Detection of a Reaction Type Missing in the Training Set An important criterion for the efficiency of AD definition methods applied to reactions is that a chemical reaction belonging to the type missing in the training set must be considered as an X-outlier (out of AD), while the reactions that belong to the type used for model building (hereafter called native reaction type) should be mostly attributed as X-inliers (within AD). This goal can be achieved algorithmically by applying Fragment Control (FC) or Reaction Type Control (RTC_cv, which is based on the analysis of the most complete representation of CGR in the form of a graph). Therefore, we have built AD definition models (RTC_cv, FC) on each of the four datasets and applied it to the other three datasets. Reaction Type Control correctly identify non-native reactions as X-outliers in 100% of cases.
Fragment Control works correctly in 99% of cases by excluding from AD non-native reactions. However, in some cases, it can make mistakes by including tautomerization reactions into the AD defined for S N 2 and E2 datasets. This can be explained by the fact that some tautomerization reactions belonging to the zwitterion-neutral molecule tautomerism have no dynamic bonds in their CGRs, and therefore no fragments with dynamic bonds can be generated. As a result, such tautomerization reactions can pass the Fragment Control filter (Figure  3 ). Thus, the FC method, from which one would expect that it can cope well with the detection of a non-native reaction type through the explicit use of structural information, in some cases does not work correctly. Since it is an essential criterion for defining AD for reactions, FC cannot be considered as robust approach for detection of novel reaction type.
Nonetheless, it can still be recommended to be applied to QSAR/QSPR models for molecular data. The previously proposed approach of Horvath et al. [40]  uses similar combinations of Fragment Control with confidence-based approaches. Based on the requirement that correct AD definition methods should identify non-native reaction types are X-outliers (out of AD), we have assumed that RTC_cv should be involved in defining AD for QRPR models. Reaction Type Control corresponds to the applicability aspect of AD definition according to Hanser et al. [12]. The RTC_cv, however, does not consider either local data density in chemical space, which corresponds to the reliability aspect of AD definition, nor decidability reflecting prediction confidence. Thus, Reaction Type Control should be used in combination with other AD definitions, which comprise composite AD definitions.
Coverage AD definitions are balanced between the coverage and the predictive performance. It is desirable that a QRPR model be applicable to the largest possible data space. Therefore, the coverage provided by AD definitions should be as large as possible. The values of coverage for all datasets for composite AD definition methods are presented in Table  3  (even though only composite AD definition methods are discussed in the paper, Table  S2  contain data for all AD definitions (not only the composite ones)). "Perfect AD model" had coverage about 98-99%, RTC1-about 93-98% for all four datasets. The latter is caused by presence of some portion of reactions that highly differ from the rest of the dataset. The coverage provided by most AD definition models optimized using the OD metric ranges from 0.
Detection of Y-outliers The detection of potential Y-outliers, i. e. , chemical objects with high property prediction errors, is the main goal of applying AD definition models. In this study, the ability to detect Y-outliers is characterized by an analogue of balanced accuracy (shorthand OD), as described in Section 2. 2. This measure is dominated by the precision of Y-outlier detection, because typically the number of Y-inliers is much greater, and therefore the true share of Y-inliers is close to 1. The values of OD estimated using the outer loop of the nested cross-validation, known also as "external cross-validation"  [5] , for all AD definition models are presented in Table  3  (results for all datasets for individual ADs are presented in Table  S2 ). The balanced accuracy values for Zero AD Models and "Perfect AD model" are 0.
5 and 1. 0, respectively, as follows from their definition. As one might expect, AD definition models with hyperparameters tuned to maximize OD detect Y-outliers much better (higher values of OD on "external cross-validation") than the AD definition models without hyperparameters or with hyperparameters tuned to maximize OIR, which tend to underestimate the number of Y-outliers. As follows from the results presented in Table  3 , 2CC*/OD, GPR*/OD, RFR_VAR*/OD are the best AD definition methods for Y-outlier detection. For some datasets, Z_1NN_cv*/OD and Lev_cv*/OD also show good performance of Y-outlier detection.
Ranking Different AD Definition Methods This study concerns four AD performance metrics, four datasets and 19 AD definition methods (results for all datasets are presented in Tables  S3-S7. For each characteristic (coverage, the ability to improve performance of QRPR model and to detect Y-outliers) and for every dataset, we have assigned a zero penalty to those AD definition models for which the value of the characteristic is in the top 50% of the best values for it, and a penalty equal to one in the opposite case. For the non-native reaction type recognition criterion, a penalty equal to one was assigned if at least one non-native reaction was predicted as X-inlier (within AD). The results obtained after ranging the sums of all penalties are presented in Tables  S3-S6. The best five AD definition methods for each dataset are presented in Table  4.
The first thing that is noticeable when considering this table is that "Perfect AD model" is always at the top of each ranked list, meaning that it is indeed a good approximation to the perfect AD definition. The most important conclusion following from the analysis of this table is that there is no single universally best AD definition method, so each dataset is characterized by its own list of preferred methods. Although there is no method that would be best for each individual dataset, nevertheless, all methods can be ranked by how often they get to the top of the lists for different datasets. To obtain such lists, we have summed the ranks for each method over all datasets. It has been found that the "Perfect AD model" is, expectedly, the first method in the ranked list again followed by ML-dependent AD definition methods RFR_VAR*/OIR, GPR-AD*/OIR and Universal AD definition methods 2CC*/OIR.
The prediction performance of the Random Forest Regressor model (see Section 2. 3) on whole test set (R 2 = 0. 60, RMSE = 0. 84) is close to those of the SVR consensus models by Gimadiev et al. (R 2 = 0. 64, RMSE = 0. 80)  [16]. The above-discussed workflow for building regression and AD models were applied without changes. The ten best-ranked AD definition models (see Section 3. 5) were trained on the data taken from the publication  [16]  and applied to the external test set. The statistical parameters of the models computed on the external test are presented in Table  5. Notice that simple approaches, which do not require any hyperparameter adjustment, such as RTC1 and BB*, are in the list of the best AD definitions as well. Although an ML-dependent AD definition model GPR-AD*/OIR significantly improves the performance of QRPR model (R 2 ), the coverage is only 17% of the dataset.
RTC1 has the same quality metrics as RFR_VAR*/OIR and 2CC*/OIR (see Line 8 in Table  5 ). Therefore, for this particular case, it is enough to use only RTC1 to define AD. Control is not at the top of the best methods for all characteristics (Tables  3  and 4 ), however, it detects correctly not only non-native reactions but also structurally new reactions of native type as X-outliers (out of AD). Its combination with other AD definition methods can catch subtle effects in reaction datasets, and therefore the above-mentioned best three AD definition approaches perform really well on the tough case considered in paper  [16] , where X-outliers are very similar to the reactions in the training set.
Conclusions In this paper, we have presented a workflow to compare and search for the best methods for defining the applicability domain (AD) of Quantitative Reaction-Property relationships (QRPR) models. However, the same technology can also be applied to QSAR/QSPR models for molecular data sets, except the consideration of reaction-specific ADs (Reaction Type Control and its combinations with other methods). The proposed methodology is quite flexible, so one can choose which AD definition method is preferable in each case. In this study, different popular AD definition methods were benchmarked. Unlike some previous studies  [9] [10] [11] , here, special attention is paid to AD definition methods in application on regression models. AD prediction is considered in this study as a binary classification (inside/outside AD).
Four datasets of reactions are used for benchmark study. Different metrics, OIR, Thus, for modelling the properties of chemical reactions, Reaction Type Control, as an approach for defining the applicability domain of the model works, strikingly well. By itself, Reaction Type Control is not at the top of the best methods for all characteristics (Tables  3  and 4 ), however, it detects correctly not only non-native reactions but also structurally new reactions of native type as X-outliers (out of AD). Its combination with other AD definition methods can catch subtle effects in reaction datasets, and therefore the above-mentioned best three AD definition approaches perform really well on the tough case considered in paper  [16] , where X-outliers are very similar to the reactions in the training set.
Four datasets of reactions are used for benchmark study. Different metrics, OIR, which was proposed earlier  [25]  and OD, which is proposed in the present publication, are used as objective functions to tune hyperparameters. OIR is shown in this paper to give models with higher coverage in comparison with OD, which gives stricter AD definitions. The latter leaves fewer datapoints within AD but provides much better predictions for them. The other metrics, including difference in R 2 for points within AD and all of them, and AUC_AD as the ranking criterion, appear not to be suitable for tuning hyperparameters. In this study, it was suggested that some filters should be applied that reveal structural novelty directly prior to descriptor calculation (since descriptor vector calculation loses structural information).
Among the latter, Reaction Type Control and Fragment Control can be noted. However, it is shown in this paper that, in some cases, Fragment Control fails to detect non-native reaction types (1% of reactions of non-native dataset), so its application as an AD definition method for reactions is undesirable. Nonetheless, it can still be recommended to be applied to QSAR/QSPR models for molecular data. Based on the assumption that correct AD definition methods should identify non-native reaction types as X-outliers (out of AD), we concluded that RTC_cv should be involved in defining AD for QRPR models. The combination of RTC_cv with other AD definition methods is beneficial because additional AD definition methods can reveal subtle effects in data distribution, like distance of the data in the training set, data density or confidence.
The wines were served at room temperature. A new bottle of each wine was opened each day that the experiment was conducted and the wines were first checked for faults by two or three experienced wine professionals. The wine samples for each stage of the task comprised 50-ml, with a new sample poured for each of the two chemosensory evaluations within each session. In Session 1, the wine samples were served in clear, Spiegelau tasting glasses (Spiegelau # 440 01 31) while in Session 2 the wines were served in black (opaque) Spiegelau tasting glasses (Spiegelau # 440 85 31). The glasses were coded with 3-digit numbers and were covered with plastic Petri dishes. In order to limit carry over effects and memory biases, all wine samples were presented in a different order specific to each participant within each session and between sessions according to a Williams Latin square arrangement generated by FIZZ software (Biosystemes, Courtenon, France).
Data A one-time snapshot survey was conducted among Luxembourg cross-border workers in 2010 and 2011. It covers 90% of the population of 146,600 cross-border workers within a commuting area that allows one return journey to work per day. The sampling frame controls for socio-demographic factors  [59]  and was spatially stratified in 25 zones  [60]  spreading over three bordering countries: France, Germany, and Belgium. Selected at random, 40,000 workers received a self-administered pen-and-paper questionnaire dealing with the usual themes of a national transport survey. There were 7235 respondents in this first phase (response rate >18%). The questionnaire had two versions: French and German. Due care was needed to ensure close adequation between terms used to label perception concepts. A second phase of the survey was undertaken in 2011 to gather the way commuters perceive their daily and residential mobility (duration, cost, and satisfaction), their beliefs about transportation modes, as well as stated incentives to use public transit.
Funding: This paper is registered as part of the Prof. Marius Thériault visiting professor scheme held at LISER in 2015 and is linked to the  CONNECTING research project  ( Consequential Life Cycle Assessment of multimodal mobility policies-the case of Luxembourg ,  FNR CORE C14/SR/8330766 ). The used database relies on the work of the CABaC 2010-2013 research project (Construction and Analysis of a Knowledge Base on mobility habits and attitudes towards energy of cross-border workers in Luxembourg,  FNR INTER/CNRS/09/01 ). The survey was funded by the  Ministry of Higher Education of Luxembourg.
Tree crop growers are particularly sensitive to commodity price fluctuations. Since 2012, rubber prices have been decreasing. This article provides a comprehensive analysis of how Thai family farms responded to that continuous decrease in rubber prices. The originality of this work was to combine a farming system approach for decision making with the sustainable rural livelihoods' framework for resource endowment and strategies to benefit from the complementarities of these two theoretical approaches. An existing database was used characterizing the rubber farms and farmers' practices in 2011 when the rubber price was at its maximum. A subsample of farmers was interviewed again in 2017 after six years of price decreases to characterize the new situation and to identify and explain the changes.
Scientific approach We used an existing database providing a characterization of the structure of a sample of rubber households in 2011 (data collected in 2012). That year corresponded to the period when the rubber price was at its highest level. Interviews were conducted with a subsample of farmers from the 2011 database to characterize their farms' structures in 2016 (data collected in 2017). That year corresponded to a low rubber price which had been decreasing since 2012. Comparing the structure of each farm between 2011 and 2016 allowed identification of the changes that had happened during that period. This basis was used to analyze the strategies adopted by each household to face the decreasing rubber price. In 2017, we used a mixed quantitative and qualitative method, with a sequential approach in two steps.
Data collection Data collected in 2012 concerned the socio-economic characteristics of the farm with a focus on land and labor, the description of the practices in the rubber plantations and the activity systems. In 2017, all the 48 farmers were interviewed twice. For the first interview (quantitative phase), the questionnaire recorded the available resources: natural, human, physical and financial assets. Social capital, which is difficult to quantify, was included in the second interview (qualitative phase). Data to characterize the rubber cropping systems and the farming systems were also collected during this first phase. The questionnaire used for this phase can be viewed using this link: https://ee. kobotoolbox. org/x/#YX8a and a synthesis of the data collected in 2012 and 2017 is presented in supplementary material (Table  1 ).
Data analysis Comparing the characteristics of each household in 2011 (available from 2012 study) and 2016 (  2017 interviews of quantitative phase), we were able to identify the changes (or non-changes) at the plots, farm and household levels. Analyzing farmers' discourses recorded during the qualitative phase of 2017 study, we could identify farmers' choices related to rubber price decrease and finally bring out the different flexibility mechanisms adopted to face falling prices. Each farmer could have adopted several flexibility mechanisms. In order to classify the smallholders according to the combinations of these mechanisms, we implemented a multiple correspondence analysis followed by a hierarchical classification using the open access R software. The multiple correspondence analysis allowed transforming nominal variables (the flexibility mechanisms adopted by each smallholder) into continuous numerical data that were used for the hierarchical classification analysis.
The hierarchical cluster analysis using Ward's minimum variance algorithm allowed building a dendrogram. Some odd-distributed variables which influenced too much the results of the statistical analysis where not considered  (Escofier & Pagès, 1988). The dendrogram was cut using a qualitative approach, at a level were resulting clusters could be characterized in accordance to the field reality. Within a cluster, the combination of flexibility mechanisms allowed to highlight the strategy adopted corresponding to the means used to reach a given objective here, to cope with the rubber price crisis. We collected and analyzed some data to understand the economic logic behind the farmers' behaviors. However, we have chosen to simplify the presentation of the results and focus on the strategic mechanisms and the main dynamics of rubber households.
Acknowledgments This study was conducted under the framework of the  Hevea Research Platform in Partnership (HRPP). It was supported and funded by the  Thai International Cooperation Agency (TICA)  and the  French National Research Agency (ANR)  under the  HEVEADAPT  project. We thank all the interviewees for their patience and kindness during the interviews as well as the staff from the  Rubber Authority of Thailand (RAOT, ex ORRAF)  for providing support in the field. This article is based on Timothée Nicod's master thesis. This study received the  AREA 2018 Award (Association for Agro Economy Research ), highlighting the quality of the work.
Despites our efforts, this sampling strategy did not provide enough unique profiles to exhaust the diversity of the whole region, as shown in the rarefaction analyses which estimated that these genotypes at the entire regional scale were sampled from an underlying population containing 6777 different genotypes (with 95% confidence limits of 3194). Saccharomyces cerevisiae strains diversity isolated from grape and vat samples. The complete dataset of 1002 grape and 289 vat isolates was compared to 33 commercials yeast starters and 35 strains isolated from various substrates  49  (Supplementary Tables  S2  and S3 ) from a tree constructed with the Bruvo distance  50  (Fig. 1A, B ). This neighbor-joining tree revealed a cluster with strains from non-wine origins (Misc. origins), while wine strains whose genome had been sequenced were found to be mixed with Bordeaux grape strains (Fig.
After the removal of identical clones detected on the same wine estate, the dataset contained 402 grape isolates, corresponding to 398 unique genotype profiles. The ancestry profiles of these individuals, including commercial strains, were then further inferred from the microsatellite dataset using the Bayesian clustering method implemented in InStruct  51. Given the microsatellite data set, the optimal number of ancestral populations inferred was K = 14 and the percentage of ancestry identified for yeast starters or grapes strains is presented in Fig. 2a ,b, respectively. The Pessac Léognan and Medoc appellations appear also to have a small proportion of specific and unique ancestral population linked with one wine estate, whereas commercial yeast starters presented a global ancestry similar to the grape isolates.
This reduced the data set from 402 to 302 grape isolates, meaning that 1/4 of the grape isolates collected were closely related to commercial strains. Basic information on multilocus genotype per samples and heterozygosity are given as supplementary information (Supplementary Table  S6 ). When analyzing the heterozygosity of the different sampled populations, we could observed for all of them a deficit in heterozygosity for all populations, very likely resulting from the S. cerevisiae life style, but two estates presented populations with a more pronounced deficit in observed heterozygosity ("pa" and "lh"), despite a similar allelic richness.
Testing for geographic differentiation. In order to evaluate the potential influence of appellation, wine estate or farming as potential factors on population structure, we performed an AMOVA (Table  3a ). The appellation and the wine estate have a highly significant effect when considered solely. However, when those factors are combined, only the impact of "appellation" on population structure remains significant whereas the "estate" factor does not (Table  3a ). This may likely come from differences in the contribution of each estate to the global variance in each appellation sampling. Indeed a DAPC performed on the genotype dataset points to the high contribution of some estates to the global variations such as "pa" or "bc" (Supplementary Fig. S5 ). The pairwise Fst distance matrix is a complementary way to display population differentiation (Table  3b ).