dataesr/scientific-paragraphs-categorization huggingface dataset:
The model-building software Modeller was chosen (version 9. 18) (28) starting from a multiple alignment of the four proteins plus the reference template, using the ClustalW algorithm for its accurate precision (with default parameters) (29) and subsequently using ESPript for its capacity to insert the secondary structure elements of the template  (30). For each protein, 100 homology models were generated, the final structure that associates the lowest values of Modeller function and DOPE score was selected, and the stereochemistry was checked using MolProbity  (31). Finally, the cofactors were added and each complex was minimized to relax local probable steric clashes, using Gromacs Charmm36 forcefield with the steepest-descent algorithm  (32). Docking of ligands. The molecule of nicotinamide was redocked in the binding pocket of TmCHMO, to check if the protocol docks it similarly to its position in the crystal structure.
0. 1) (37). Benjamini-Hochberg (BH) P value correction was duly implemented using the multcomp (v1. 4. 16) package  (38, 39). The same package was used to produce general linear hypothesis post hoc tests. Data wrangling and visualization of results were handled with Tidyverse (v1. 3. 0) (40). R source codes are available upon request.
ACKNOWLEDGMENTS We acknowledge  Samantha Samson  ( INRAE ) and  Marco Bellinzoni  ( Institut Pasteur ) for fruitful discussion and Patricia Constant (IPBS) for contribution in methodology. We also thank  Laure Souillès ,  Aurore Pérault , and  Adrien Delpal  for their technical help in the early phase of the project. We are grateful to the  Ministry of Higher Education, Research and Innovation  for fellowships to N. T. , D. L. , and M. C. and to the  Slovak Research and Development Agency  (grant no. APVV-19-0189 ).
Ridge sails and keels affect the drag by winds and currents. At the same time ridged ice significantly impedes navigation in the Arctic  (Lindsay and Stern, 2003). Given the importance of sea-ice fracturing for air-sea-ice interface processes, marine life and navigation, its accurate monitoring and forecasting is in great demand. Observations of cracks can be performed using satellite remote sensing by retrieval of high-resolution sea ice drift from Synthetic Aperture Radar (SAR) data and computation of sea ice deformation components  (Kwok et al. , 1990). The Radarsat Geophysical Processor System (RGPS) dataset was the first attempt to systematically observe sea ice deformation on high spatial resolution (10 km) and with high frequency (3 days) over a long period of time  (winters 1996-2016)   (Kwok, 1998 ).
Satellite observations of sea ice deformation We used the sea ice drift and deformation dataset from CMEMS  (Saldo, 2020)  acquired in January 2021. The dataset comprises gridded products derived from Sentinel-1 synthetic aperture radar (SAR) images, with 10 km spatial resolution and 12 hours frequency. The spatial coverage of the product is irregular -the East Siberian, Laptev and Kara sea and the polar gap (north of 87 • N) are never covered, while other Arctic regions are observed nearly every day.
(2016). It was only calibrated to be compared to statistical properties of sea ice deformation derived from the RGPS observation dataset  (Kwok et al. , 1990)  and on large-scale sea ice thickness and drift time series. Therefore, it is not tuned for predicting the exact position of cracks in the sea ice cover, which may impact the predictability we obtain in this study. The BBM rheology can be further tuned and compared to the modified Elasto-Visco-Plastic rheology (mEVP,  Bouillon et al. , 2009)  that is already an available option in neXtSIM  (Olason et al. , 2022)  for estimating the impact of rheology on the sea ice predictability. -Model numerics. In neXtSIM, the model equations are derived and solved on a triangular mesh that deforms with the ice motion in a pure-Lagrangian approach.
S. respondents, n = 359, MTurk panel members with master's qualification (Study 2) -Exploratory factor analysis using SPSS (v. 25), 45 items in 9 factors -Confirmatory factor analysis using AMOS (v. 25), 36 items in 9 factors -Empirical assessment of the formative measurement model specification using confirmatory tetrad analysis (CTA-PLS) -Common method bias tests (CFA marker variable technique, VIF) -Nomological validity assessment of the newly developed OCX model -Predictive relevance ( Q ²) tests using blindfolding and PLSPredict procedures and SAT, LOY, and WOM outcomes. Phase 4: Measurement model validation and finalization -Survey instrument development using Qualtrics online tool -Nine pretests of the survey instrument -Data collection: U. S. respondents, n = 447, Qualtrics panel members (Study 3) -Assess the new data set for model validation (EFA, CFA, CMB) -OCX measurement model assessment using  SmartPLS (v.
1 ata analysis. Following  Reynolds and Olson (2001)  , we analyzed the laddering data (n = 79) in three steps. First, after exporting the data from Qualtrics into NVivo software, we content-analyzed the data to develop an overall sense of the types of elements elicited. Second, we defined meaningful categories of attributes (A), consequences (C), and values (V). Third, in an implications matrix, we display direct and indirect links across A → C → V elements, then develop an aggregated HVM  ( Reynolds and Olson 2001 )  that graphically depicts the dominant perceptual patterns in the data and provides an estimate of the cognitive structure. Gengler and Reynolds (1995)  recommend limiting laddering analyses to fewer than 50 categories. Relying on a series of reviews by two expert judges, we identified 39 categories (see Web Appendix W1): 14 attributes (Table  W1.
2011 ). We used Qualtrics surveys to collect data from academic experts (content validity) and omnichannel customers (face validity). Following recommendations from MacKenzie et al. (  2011 ) , for the content validity assessments, we developed an online survey with the initial OCX measurement items in the first column and the conceptual dimensions in 14 additional columns. The operational definitions of the dimensions appear at the top of each column. The online survey was configured to display five items at a time for ratings, and all ratings were forced. In addition, a textbox for each item allowed the academic experts to provide comments and justify their ratings; this element was optional  Netemeyer et al. (2003). suggest using a three-point categorization rating by at least five expert academics, so the survey was configured to permit academics to rate each item as "clearly representative," "somewhat representative," or "not representative" of each conceptual dimension.
The net result was a refined pool of 87 items with at least 6 measurement items for most dimensions and no less than 4 for any dimension. From this item pool, we developed an online survey using the Qualtrics tool to conduct the face validity tests. Here,  Netemeyer et al. (2003)  recommend three-point categorization ratings from at least five target population respondents, so the online survey again was configured to display the definition of each dimension and request a rating of each item as "clearly representative," "somewhat representative," or "not representative" of the dimension. We gathered responses from omnichannel U. S. customers on MTurk but excluded any respondents who had participated in Phase 1. The data collected from seven omnichannel customers did not prompt any changes to the initial pool of items.
Phase 3: measurement model refinement (Study 2) Data for this phase were collected using the online survey we had developed in Qualtrics. With screening questions and an IP check, we recruited 365 omnichannel customers on MTurk who had earned master's qualifications. No respondents who had participated in previous stages could participate. As suggested by  Netemeyer et al. (2003)  and  MacKenzie et al. (2011)  , we carefully sought the most appropriate survey configuration (e. g. , response scale type, control for common method bias, motivate respondents). Web Appendix W2 outlines the survey design and demographics; we removed 6 responses due to extremely low standard deviations or short survey completion times, resulting in 359 respondents. In addition to the initial pool of OCX measurement items, we added items for WOM, satisfaction, and loyalty outcomes, as endogenous variables, to provide an initial test of nomological validity with the model refinement data set, as well as to enable a comparison of the refinement (this phase) and validation (Phase 4) data sets ( MacKenzie et al.
Exploratory factor analysis The exploratory factor analysis (EFA) of the Study 2 data, using SPSS, involved a series of iterative analyses (Web Appendix W3) to establish a parsimonious and reliable factor structure for the OCX measurement model. First, we conducted a test of normality to select an appropriate factor extraction method for the subsequent analysis  ( Fabrigar et al. 1999 ). Both Shapiro-Wilk and Kolmogorov-Smirnov significance values are less than 0. 05 for all items, so the data are not normally distributed, and principal axis factoring extraction appears appropriate  ( Fabrigar et al. 1999 ). Second, the first-order dimensions are conceptualized (Phase.
Common method bias tests We conducted several statistical tests to ensure the study was not contaminated by common method bias (CMB). First, a principal component analysis in SPSS with rotation set to none and factors to extract set to 1 reveals that the maximum variance explained by a single factor is 31. 49, so CMB is unlikely ( < 50), according to Harman's single-factor test. Second, we apply a marker variable technique ( MacKenzie and Podsakoff 2012 ) and test for correlations between the dimensions of OCX and a conceptually unrelated, social desirability response scale using an AMOS-based CFA (i. e. , the marker variable should not correlate with a variable in the survey if there is no theoretical relationship between them). As expected, the analysis does not show any association between the OCX dimensions and the marker variable (Web Appendix W5).
Nomological validity assessment of the newly developed OCX model In Phase 2, we tested the association of the newly developed OCX model with satisfaction, loyalty, and WOM, using Study 2 data. In Table 2 , all goodness-of-fit (GoF) indices are above conventional cut-off values  ( Hu and Bentler 1999 ) , and the OCX model with 36 items contained in 9 factors offers the best model fit. The comparison of commonly used GoF indices implies that the data fit the proposed model reasonably well. In Table  3  , the uniformly high and significant EFA, CFA, and PLS item-construct loadings suggest that the first-order dimensions of OCX are reflected well by the corresponding measurement items. In Table  4  , the ratios are less than 0. 9 across all dimensions, which confirms discriminant validity  ( Henseler, Ringle, and Sarstedt 2015 ).
Phase 4: measurement model validation and finalization (Study 3) With another data set (Study 3), representing responses from the relevant population, we validate the newly developed measurement model. It is appropriate to include at least one data set with responses from non-MTurk panel respondents in any study involving multiple stages and data sets  ( Hulland and Miller 2018 ). Therefore, we collected responses from April-May 2019 from Qualtrics panel members in the United States. In this study, in addition to satisfaction, loyalty, and WOM 2 A blindfolding test predicts original values by reusing the sample after a systematic pattern of data point elimination, based on an omission distance (D). For example, an omission distance of D = 6 implies that every sixth data point is omitted in each blindfolding round, after which the test predicts every data point of the indicators used in the measurement model for a selected latent variable  ( Hair et al.
Robustness check using FIMIX-PLS and PLS-MGA To confirm the robustness of the structural model, we check for unobserved heterogeneity, to ascertain if an analysis of the entire data set is reasonable  ( Hair et al. 2017   (Henseler et al. 2015). The PLS-SEM values are from SmartPLS, with weighting scheme = path, iteration = 1000, complete bootstrapping with 5000 subsamples, test type = two-tailed, and repeated indicators for the second-order OCX. All values are significant at p =. 001. Respondents rated one random SAT, WOM, LOY, and TRUST item at a time on a seven-point Likert response scale, and each point was clearly labeled, from "strongly agree" to "strongly disagree," with "neither agree nor disagree" as the midpoint. Each SOW item was measured using a different five-point response scale.
ARTICLE IN PRESS JID:  RETAIL [m5+; April 3, 2022; 7:56 ]  (i. e. , more than one significant segment is present in the observations) that produce substantially different structural model estimates. Therefore, we apply a finite mixture partial least squares (FIMIX-PLS) technique in SmartPLS (v. 3. 2. 8), using data set 2; the results are available in Web Appendix W7. To determine the number of segments to retain, we check the fit indices for solutions with one to five segments (Table  W7. 1). The indices do not yield conclusive evidence of the existence of more than one segment. According to the relative segment sizes across different FIMIX-PLS solutions (Table  W7. 2), any solution with three or more segments should be discarded. A three-segment solution would involve a segment with only 81 (18% of 447) observations, less than the minimum sample size required for the OCX model assessment.
Revalidation of OCX measurement model To revalidate the OCX measurement model, we employ several assessments with the new data set. First, EFA with maximum likelihood extraction and the number of factors fixed to 9 produced a factor structure identical to that in Phase 4; the SPSS-based EFA loadings are available in Table  3  (Study 4 columns). Second, the CFA in AMOS produces GoF indices that exceed conventional cut-off values, so the data fit the OCX model that features 36 items classified into 9 factors well ( Table  2  , Study 4 columns). Third, the uniformly high, significant CFA and PLS item-construct loadings confirm that the first-order dimensions of OCX are well reflected by the corresponding measurement items ( Table  3  , Study 4 columns). In Web Appendix W10, Table  W10. 2, the ratios are less than 0.
g. run, knock) regardless of whether they have a perceived endpoint or not. The perception of telicity and duration is the outcome of the entire verbal phrase, and not solely the verb's features  (Krifka, 1998). Besides, the context can also place constraints on the aspectual class of a verb  (Siegel, 1998). Therefore, making sound judgments on aspectual features such as telicity and duration, especially in a morphologically-poor language like English, is not always an easy taskour datasets in Section 4. 1 provide some examples of sentences where these features are hard to assess, even for a human. Aspect has been exploited for tasks where semantic knowledge is necessary, since it provides information on temporal relations  (Costa and Branco, 2012) , textual entailment  (Hosseini et al. , 2018; Kober et al.
, 2019)  and event ordering  (Chambers et al. , 2014). In recent years, transformer-based models have shown great success in NLP tasks which traditionally require in-depth language analysis and complex strategies on capturing dependencies, semantic information, and world knowledge. However, it remains unclear whether the success of these models is due to a genuine capability to accurately model linguistic meaning, or whether the models are just very good at picking up statistical correlations, but fail to capture fine-grained semantic distinctions  (Ettinger, 2020). With this research question in mind, our goal is to investigate whether transformer-based architectures (both with and without fine-tuning) are able to capture the semantic information related to telicity and duration. To do so, we make use of two datasets annotated for telicity and duration  (Friedrich and Gateva, 2017; Alikhani and Stone, 2019) , and we conduct a range of experiments using several pretrained transformer architectures in two languages (English and French).
We extend our experiments from  Metheniti et al. (2021) , where we only made use of the Friedrich and Gateva dataset and only in English. We aim to explore the capabilities of transformer architectures in classifying aspect beyond mere quantitative evaluation: we made custom qualitative datasets in order to observe how complex context, verb tense and prepositional phrases affect classification. 1 e find that classification with fine-tuned models is very successful-both for telicity and duration-and this success can be largely attributed to the knowledge built up during pre-training, as contextual word embeddings by themselves are already quite capable of capturing this information. We noticed that complex cases where the context was conflicting with the verbal aspect were harder for the models to classify, and we provide evidence that misclassification in complex sentences is related to verb tense and word order.
3 Previous Work  Siegel and McKeown (2000)  were the first to propose natural language processing methods for aspectual classification; they used decision trees, genetic programming, and logistic regression to locate linguistic indicators of stativity and completeness, and observed that there was an improvement on the classification of these features, especially with supervised methods, compared to unsupervised classification. Friedrich and Palmer (2014) use a semisupervised approach for learning lexical aspect, combining linguistic and distributional features, in order to predict a verb's stativity/duration, and also released two datasets of annotated sentences for stativity. Friedrich and Pinkal (2015)  extended this approach by classifying verbal lexical aspect into multiple categories of duration, habitual/episodic/static, and  Friedrich et al.
(2016)  expanded their datasets and categories, achieving 76% accuracy on supervised classification compared to the 80% of their human baseline. In their most recent work,  Friedrich and Gateva (2017)  have released two datasets in English with gold and silver annotations of telicity and duration (gold is human annotated; silver is obtained from parallel English-Czech corpora where aspectual features were extracted from Czech morphological markers). With these datasets and an L1-regularized multi-class logistic regression model, they report significant improvement on automatic telicity classification. Loáiciga and Grisot (2016)  exploit telicity in order to improve on French-English machine translation; they are using verb classification of telicity (defined as boundedness) and notice improvement on the translation of tense.
English Datasets Telicity and duration-annotated sentences will be used as two separate datasets for our experiments. The two datasets from which we are sourcing sentences are constructed by  Friedrich and Gateva (2017)  and by  Alikhani and Stone (2019). Friedrich and Gateva's dataset 2  includes goldand silver-annotations of telicity (telic/atelic) and duration (stative/durative). The gold annotations are based on the MASC dataset  (Ide et al. , 2008) , while the silver annotations were crafted on the basis of the InterCorp parallel corpus of English and Czech  ( Čermák and Rosen, 2012) , extracting the annotations from the Czech morphological markers of telicity and duration and applying them to the English translations. Each annotation corresponds to a specific verb in each sentence and not the entire clause.
The "Captions" dataset 3  by Alikhani and Stone (2019) was created from five image-text corpora, in order to study inferential connections in sentences. It has been annotated for telicity (telic/atelic) and duration (stative/durative/punctual) based on the verb's aspect. Even though the focus of the original work was on the head verb of each sentence, the verbs were not separately annotated, therefore we used dependency parsing with spaCy  (Honnibal et al. , 2020)  in order to extract the verb and its position for our experiments. We noticed some inconsistencies in annotation, which we corrected, and we also excluded the sentences annotated with the punctual label, since there were too few sentences to warrant a third category or to combine with the durative label. In Table  1  we present the sizes of the datasets and our final dataset.
We split this dataset in training, validation and test sets with a ratio of 80-10-10%. We also created some smaller datasets for testing purposes, in order to observe specific phenomena in our models. First, we created forty sentences annotated for telicity, and forty for duration, a sample of which can be found in Table  2. We also crafted "minimal pairs" of sentences with telicity annotations, where each pair includes the same verb but in a context that has a different degree of telicity (see examples in Table  3 ). We also created variations for some of these sentences, moving prepositional phrases to different positions in the sentence or changing the verb tense without changing the meaning or the degree of telicity, in order to test whether the models are sensitive not only to specific verbs but also word position and tenses (see Table  4 ).
For base models each training epoch took ~3 minutes and for large models ~7 minutes, on one GPU system of a computing cluster, with CUDA acceleration. As baselines, we make use of two standard binary classification models trained and tested on the same sets: a simple bag-of-words logistic regression model, implemented with the Python library scikit-learn  (Pedregosa et al. , 2011)  with default parameters and data scaling, and a one-layer convolutional neural network model (CNN) implemented with Pytorch  (Paszke et al. , 2019)  and trained for 50 epochs, which is commonly used for text classification tasks  (Kim, 2014). The CNN model is trained with the fastText 300-dimensional embeddings  (Bojanowski et al. , 2017) , embedding dimension of 300, filter size of  [3, 4, 5] , 100 filters per dimension, dropout rate of 0.
Classification in French We also wanted to examine whether telicity and duration were classifiable in a different language with transformer models. We chose French, as it differs from English in the way verb tenses are formed (conjugation, compound tenses) and used (present continuous is morphologically the same as present simple), but it does not have a dedicated morpheme to expressing telicity such as Finnish and Czech. We are using the two monolingual French transformer models available from the transformers library, CamemBERT  (Martin et al. , 2020)  and FlauBERT  (Le et al. , 2020). CamemBERT is built based on the RoBERTa architecture and trained on monolingual data. FlauBERT is a BERTmodel trained with multiple, heterogeneous corpora, and a more extensive tokenization procedure. Since there are no available annotations of telicity and duration in French, we translated our English datasets with the DeepL translator 4  and reviewed manually a portion of the datasets (200 sentences) for translation accuracy and annotation correctness.
Our average score for the accuracy of the machine-translated sentences was 88% and for the accuracy of the annotated labels was 73. 5%. We also extracted the verb-head word of each sentence with the spaCy dependency parser to train with/without verb position, but we are not entirely confident in the results, therefore we are not testing the models' verb embeddings per layer and the unseen verbs of the test set, as we did in English. We use the resulting datasets to fine-tune the FlauBERT and CamemBERT models, and assess their abilities on aspectual classification. In addition, we manually translated our qualitative test sets and made appropriate changes (when verb tense did not convey the desired telicity, for example), and in lieu of the 80 sentences on variations of word order and verb tense, we created more minimal pairs with variations on prepositional phrases.