dataesr/scientific-paragraphs-categorization huggingface dataset:
F I G U R E 2 Intra-and cross-format predictions. (a) Comparison of the prediction accuracies when applied to the math word (MW) test dataset using operator features. The intraformat prediction accuracies of the MW model (denoted as R WW , blue) and the cross-format math expression (ME) model (denoted as R EW , orange) are mapped onto the cortical surface of subject ID01. Regions in which activity was predicted, regardless of the stimulus format, are shown in white. Only significant voxels (P < 0. 05, false discovery rate [FDR] corrected) are shown. (b) Comparison between the prediction accuracies of the ME test dataset. Intra-format prediction accuracy, using the ME model (denoted as R EE , blue) and cross-format prediction accuracy, using the MW model (denoted as R WE , orange) are mapped onto the cortical surface.
Acknowledgements This research project and the sixth author was supported by the  European Commission , having received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  792432. The first author was supported by the  Austrian Science Fund (FWF) :  P 31327-N32. The second author was supported by  CONACYT  Project  299261. Partially supported by  PAPIIT IN108320. The fifth author would like to acknowledge the support of the  bilateral  project  ANR-17-CE40-0036  and  DFG-391322026  SYMBIONT. 22 This work was started during the Tropical Differential Algebra workshop, which took place on December 2019 at  Queen Mary University of London. We thank the organizers and participants for valuable discussions and initiating this collaboration.
Acknowledgments This work has been carried out within the framework of the  EUROfusion Consortium , funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No  101052200  EUROfusion). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them. This work has been also supported by  ANR  project  MUFFIN  ( ANR-19-CE46-0004 ) and by  Centre Henri Lebesgue , programme  ANR-11-LABX-0020-0.
Methods Participants Twenty-eight participants took part in the study. The data of three participants were excluded from the analysis for having poor accuracy in at least one block (<70%). Therefore, we analyzed the data of 25 participants (18 female, 7 male; M age (SD) = 31. 2 (11. 3), range = 18-54). All participants had normal or corrected-to-normal vision and no color-vision deficits and gave informed consent to participate in this experiment for course credits or 8€.
Procedure The same procedure was used in all tasks. Stimulus presentation and response collection were implemented in MATLAB using the Psychophysics Toolbox  (Brainard, 1997; Kleiner et al. , 2007; Pelli, 1997). Stimuli were presented in the center of the monitor and were 22 mm high and 15 mm wide. Participants sat at approximately 50 cm from the monitor (visual angle: 2. 5°× 1. 7°). Each trial started with a fixation mark (#) presented for 500 ms, followed by the target, which remained on the screen until the response or for 1,300 ms. Participants were instructed to press a key on the left ("left-control" with the left hand) or right ("enter" on the numpad with the right hand) sides of the keyboard according to the block-specific instructions. The two keys were approximately 40 cm apart. Following the offset of the target, the next trial began after an intertrial interval of 500 ms consisting of a black screen.
Results Analysis 1 was performed in R (R Core Team, 2021) and RStudio  (RStudio Team, 2021). The data and the annotated R code used for the analysis are available at the Open Science Framework  (Didino, 2023). Participants had a very high accuracy in all tasks (parity judgment: M = 0. 94, SD = 0. 04; easy color task: M = 0. 97, SD = 0. 03; 1 We used the following open-source packages: bayesplot  (Gabry & Mahr, 2022) , brms  (Bürkner, 2017 (Bürkner, , 2018 (Bürkner, , 2021)) , broom  (Robinson et al. , 2022) , ggpubr  (Kassambara, 2020) , here  (Müller, 2020) , janitor  (Firke, 2021) , knitr  (Xie, 2014 (Xie, , 2015 (Xie, , 2021)) , kableExtra  (Zhu, 2021) , lme4  (Bates et al. , 2015) , magrittr  (Bache & Wickham, 2020) , plotly  (Sievert, 2020) , posterior  (Bürkner et al. , 2022) , tidybayes  (Kay, 2022) , and tidyverse  (Wickham et al.
, 2019). The raincloud plots were generated with the code from  Allen et al. (2021). difficult color task: M = 0. 92, SD = 0. 05). Accuracy data are likely affected by a ceiling effect and thus will not be further analyzed. Trials with incorrect (1787 trials, 5. 73%) or omitted responses (158 trials, 0. 51%) or RTs faster than 250 ms (29 trials, 0. 09%) were excluded from the analysis. For each participant and each task, correct trials with RTs more than 3 SD from the mean were considered outliers and excluded from the analysis (145 trials, 1. 48%, for parity task; 156 trials, 1. 55%, for easy color task; 157 trials, 1. 65%, for difficult color task). Mean RTs (in ms) across tasks are reported in Table  1. To measure the SNARC effect, we calculated the dRT as mean RTs for the right hand minus mean RTs for the left hand, separately for each target number, task, and participant  (Fias et al.
, 1996 ; see also  Pinhas et al. , 2012; Tzelgov et al. , 2013). The distributions of dRTs are reported in Figure  1. The dRTs were analyzed with a hierarchical Bayesian model 2 with normal likelihood function. The priors and the other specifications are reported in annotated R code in the OSF deposit  (Didino, 2023). The model likelihood was defined as follows: dRT ∼ Normalðμ; σÞ (1) where μ ¼ 0 þ ðtask=target cÞ þ ð0 þ ðtask=target cÞjsjÞ (2) The variable task includes three levels (parity, color difficult, color easy). The variable target_c is the centered version of the variable target and assumes the values À4, À3, À2, À1, 1, 2, 3, 4 (corresponding to 1, 2, 3, 4, 6, 7, 8, 9 in the   2 We also examined whether the model misfit the by-subject error. We compared the posterior distribution of the standard deviation and the observed standard deviation for each participant.
The secondary tasks have not been further analyzed because they were only used to encourage the participants to process additional features of the stimulus (i. e. , parity or font). The data and the R code used for the analysis are available at the Open Science Framework  (Didino, 2023). The following analyses were performed on the color task trials only. Trials with incorrect (1,042 trials, 6. 78%) or omitted responses (400 trials, 2. 6%), or RTs faster than 250 ms (77 trial, 0. 5%) were excluded from the analysis. For each participant and each condition, correct trials with RTs more than 3 SD from the mean were considered outliers and excluded from the analysis (53 trials, 1. 46%, for parity cue-first; 30 trials, 0. 86%, for parity target-first; 55 trials, 1. 53%, for font cue-first; 30 trials, 0.
84%, for font target-first). Mean RTs (in ms) across conditions are reported in Table  2. Separately for each target, condition, and participant, the dRTs were calculated as mean RTs for the right hand minus mean RTs for the left hand  (Fias et al. , 1996 ; see also  Pinhas et al. , 2012; Tzelgov et al. , 2013). The distributions of dRTs are reported in Figure  5. The dRTs were analyzed with a hierarchical Bayesian model 3 with a normal likelihood function. The priors and the other specifications are reported in annotated R code in the OSF. The model likelihood was defined as follows: dRT ∼ Normalðμ; σÞ where μ ¼ 0 þ ðcondition=target cÞ þ ð0 þ ðcondition=target cÞjsjÞ (4) The variable condition includes four levels (parity cuefirst, parity target-first, font cue-first, and font target-first).
, 2010; Turner and Van Zandt, 2012). Instead, the model returns summary statistics calculated from a hypothetical dataset and which are compared to the observed values. This framework allows a simulation of complex processes behind an observed outcome and is particularly used in population genetics  (Estoup et al. , 2001; Nielsen and Beaumont, 2009)  or epidemiology  (McKinley et al. , 2018). In ecology, the method was used to infer speciation rates and immigration of species under a neutral ecological model  (Jabot and Chave, 2009) , to test the existence of a socially induced reproductive synchrony  (Koizumi and Shimatani, 2016) , or to determine the species richness  (Solow and Smith, 2009). The sea lamprey, Petromyzon marinus, is an anadromous jawless fish. Both males and females build nests, often in pair but sometimes as groups of several individuals.
ABC framework Our model was built using the Approximate Bayesian Computation (ABC) framework  (Beaumont, 2010; Csilléry et al. , 2010)  and the ABC_sequential function within the EasyABC package  (Jabot et al. , 2015)  for R (version 4. 1. 2; R Core Team, 2021) that implements the sequential algorithm from  Lenormand et al. (2013). ABC framework was chosen as it allows to infer the posterior distribution of key parameters À here population size À of a model whose likelihood is too complicated to derive, here an individual-based model of a sea lamprey spawning season. Using a set of values for individual parameters, we simulated the individual nesting history of males and females spawning lamprey, which may build, or not, a nest each day d of the season, depending on their parameters and their previous spawning activity on day 1 to d-1.
Those sex-dependent parameters were the maximal number of frequented nests, the number of individuals in each nest, the delay of arrival on the spawning ground, and the residence time. The occupation duration of a nest was also considered. At the end of the simulation, the model produces some summary statistics (i. e. , the maximum, median, mean, Q 25 and Q 75 of the number of nests built on each day of the season). After k simulations of spawning seasons, each with a different number of individuals N, the model returns a posterior distribution of the most likely number of individuals N estimate depending on the summary statistics really observed during the spawning season. An ABC model initially generates a sample of model parameter values (often also called particles) from the prior distribution and selects the values leading to model outputs (resumed as summary statistics) satisfying a proximity criterion with the target data (data observed on the field for example).
Parameter distributions and priors All parameter distributions were determined using data from  Dhamelincourt et al. (2021a) , available in Data INRAE (clickable link). These parameters are all needed to run the model as they are the basis of the realistic arrangement of individuals in the nests, simulated by the model. Briefly, these data were obtained from a daily survey of a 1 km long stretch of the Nive river (France) on which 202 nests were found over a 47-days long spawning season. The number of males and females was counted on 69 nests. A total of 114 individuals were captured with a fishing net when first observed on a nest, marked and observed again (for 60 of them) on either the same or subsequent nests. Number of nests and number of individuals per nest followed a zero-truncated negative binomial distribution (generated using the rztnbinom function of the countreg package which generates values according to the distribution of the observed data; see  Zeileis et al.
Sampling schemes simulation To test how the accuracy of the estimated number of spawners varied with the way field data are collected, we simulated a variety of sampling schemes, in terms of both frequency and regularity of field work. Four frequencies were simulated: (a) every day of the season, (b) half of the days, (c) once a week, (d) once in two weeks. For each the last three frequencies, two regularities were simulated: (1) regular: every other day, every seven days, or every 14 days, or (2) randomized across the season (e. g. , possibly two days in a row followed by 12 days off, for the weekly frequency). This combination of frequency and regularity therefore resulted in seven sampling schemes. First, a full spawning season was simulated and served as a basis. For all sampling schemes, the summary statistics were calculated on a subset of this full spawning season according to the sampling scheme considered.
The model code being freely available and the dedicated application allowing an easy modification of the parameters, we think our model as being a baseline to be improved by complementary studies. Although, even if some parameters (obtained from  Dhamelincourt et al. , 2021a)  used in our model are consistent with observations made in other populations (e. g. ,  Applegate, 1950  for the number of individuals per nest in the Ocqueoc river, MI, USA;  Gardner et al. , 2012  for the duration of individual activity in the Sedgeunkedunc stream, ME, USA), some parameters may vary across populations, depending on the genetic background of the population, habitat features, or the local density of lamprey. Additional studies describing lamprey spawning behaviour in different localities will therefore be welcome to refine our model and possibly adapt it to local situation.
Determining a trade-off between cost and accuracy requires thinking about the limit one would like to set on the bias of the estimate. According to our results, it appeared necessary to count the nests at least half the days of a spawning season, days randomly chosen or with a regular once in two days step. For both of them, the bias did not exceed 13%, while it reached up to 50% on a weekly basis. However, the operator is not constrained by the day to choose as long as he surveys half of the spawning season. To illustrate the sensitivity of our model's performance to sampling design, we only simulated six sampling schemes according to frequency and evenness of nest counts. However, a potential user of our model could also simulate a raw dataset inspired by the probable magnitude of spawners abundance, apply several custom sampling schemes based on his constrains (e.
g. , operator availability, upper limit on total number of days in the field), and run our model on each generated dataset. This would allow assessing the accuracy and precision of the estimate associated to each considered sampling, and schedule field season accordingly. In order to make the model easily usable by any people interested in sea lamprey population management, even without R coding knowledge, we developed a user-friendly web application (https://mdhamelincourt. shinyapps. io/Lamp roie_tracker/;  Chang et al. , 2015). The simplest way to use it is to upload a dataset and launch the analysis, using the default parameter values given in the present article. A. csv dataset with a first column corresponding to the days of monitoring and a second column indicating the number of active nests counted each day is required.
Column names do not matter but it is necessary to write 'NA' for the non-monitored days if they appear in the dataset. The 0 value is considered as a day without active nest. This dataset must be loaded in the tab "Chargement des données" where the user can control the correct form of the data (some options are helpful in the loading of the dataset when necessary). Then, one just launches the analysis in the tab "Lancement de l'analyse" and waits a few minutes until the end of the computation. A plot of the posterior distribution of spawners abundance is then displayed and the user can save it or simply note the median and 95% confidence interval of the spawners abundance estimate. As we wanted the model to be adjustable to the study site and the characteristics of the population studied, the user can modify all the parameters of the model in the tab "Paramètres".
However, as the estimates can drastically change depending on these values, the user must have reliable information in order to change them. If supplementary information needs to be included in the model, the code is freely available at (https://github. com/ Marius-Dhamelincourt/Lamproie-tracker; license CC BY-NC-SA) and can be used outside of the application. We did not include this possibility inside the application as it would have complicated the ease of use. In addition, it may be possible to infer more parameters than solely spawners abundance. For example, the model could estimate any other parameter used to simulate the spawning behaviour (see Tab. 1), such as the residence time. However, the objective of our model is to specifically estimate the abundance. Furthermore, the addition of parameters to estimate may require more computation time while providing information that would not be of interest to managers.
| Specimen sampling The braincases of 64 extant specimens were examined for 3D reconstructions of their endocranial structures (Table  1 ). Our sample comprises 17 extant species, representing all but one (Paleosuchus) of the extant genera currently recognised: Alligator, Caiman, Melanosuchus, Crocodylus, Osteolaemus, Mecistops, Gavialis and Tomistoma. Our dataset includes hatchlings, juveniles, sub-adults and adults to account for ontogenetic changes, especially in Alligator, Caiman, Crocodylus and Gavialis for which we had enough specimens to describe ontogenetic series (Table  1 ). All species but Caiman yacare, Melanosuchus niger, Crocodylus acutus, Crocodylus halli and Crocodylus palustris, are represented by at least two adult or subadult specimens. All specimens were retrieved from museum collections or online databases and no living specimens were used or euthanised for the purpose of this study.
| X-ray micro-computed tomography Thirty specimens were scanned at the Laboratoire Mateis (INSA), using a V|tome|X CT instrument (GE Sensing & Inspection Technologies Phoenix X-Ray) for skulls smaller than 50 cm, and a DTHE (Double Tomographe Haute Energie by RX Solutions) for larger skulls. One specimen was scanned at MNHN Viscom France; two at the ISEM (RX Solutions EasyTom 150); the eight specimens from the SMNK were scanned at the KIT IPS and seven were scanned at the NHMUK (Nikon Metrology XT H 225 ST). The rest of the specimens comes from CT data available in the online databases Morphosource (https://www. morph osour ce. org/) and Digimorph (http://www. digim orph. org/). Additional information on the μCT parameters, including voxel size, can be found in Data S1.
| Image processing Raw CT data were imported into the software ImageJ  (Schneider et al. , 2012) , where 16-bit image stacks were converted into 8-bit to reduce file weight and contrast was enhanced between cranial bones and cavities. The resulting files were imported into the software Avizo Lite (version 7, 8. 1, 9 and 9. 5) for digital segmentation, volume rendering and visualisation of the endocranial structures. Segmentation was performed by multiple authors, and anatomical descriptions are outlined in Section 3. 1. It was either done semi-automatically with inter-slice interpolation for most extant specimens, or manually slice by slice for subfossil specimens due to the sediment filling. We follow the nomenclature of  Kuzmin et al. (2021)   (and references therein)  regarding endocranial terminology.
Due to the size limitations of the Phoenix instrument, μCT acquisition of large specimens was, in this case, restricted to the basicranium: metrical measurements were thus obtained on the complete skulls when they were available. The most recent μCT acquisitions were conducted on the DTHE (INSA Lyon), where size limitations were no longer a problem, allowing for the largest skulls in our database to be μCT-scanned entirely. Total skull length (SL) was measured from the anterior margin of the premaxillae to the posterior margin of the supraoccipital. All volume renderings are available on MorphoMuseum (https://www. morphomuseum. com/) for 3D visualisation  (Perrichon et al. , 2022).
| Ontogenetic stage determination Crocodylians undergo continuous growth during development, precluding a clear osteological assessment of ontogenetic stages and making it difficult to attribute precise size boundaries which would also differ for each species  (Morris et al. , 2019; Schwab et al. , 2021). Therefore, extant specimens were classified into four size classes (hatchling, juvenile, sub-adult and adult) based on their total SL and respective genus (see Table  1 , Table  S1 ), in order to compare sinus shape between specimens of different sizes. Our dataset includes eight hatchlings, 13 juveniles, 14 sub-adults and 34 adults (Table  1 ). As rostrum size is greatly variable between species, and as the development of each individual of a species can also be different depending on food access and environmental condition, we also provide the skull width (SW) for each specimen (or the braincase width for incomplete fossils) in addition to the inferred ontogenetic stage when addressing a specific individual.
| Landmark protocol The 3D mesh models of the intertympanic sinus system were exported from the software Avizo as STL files, to be quantified using a 3D geometric morphometric approach. Twenty-five type II landmarks were placed on the surface of the 3D models using the software MorphoDig 1. 6. 4 (Lebrun, 2018). The landmarks were chosen to reflect the shape and extension of the pneumatic cavities and were placed on the maximal curvatures and maximum extensions of the different structures (Figure  2 , Table  3 ). Using this so-called 'homology-free' method enables us to compare shape even if a given structure is seemingly absent in a given specimen, by using partly degenerate configurations of landmarks: that is, when a structure is extremely reduced or absent, all landmarks characterising it are placed in the same location, following  Polly (2008)  and  Klingenberg (2008).
| Data analyses All subsequent analyses were performed in R 4. 0. 5 (R Core Team, 2021) using the package geomorph 4. 0. 0 for geometric morphometric analysis  (Adams et al. , 2016 (Adams et al. , , 2022;; Adams & Otárola-Castillo, 2013). Generalised least-squares Procrustes Analysis (GPA) superimposition was performed on the landmark dataset with the function gpagen to correct for differences in size and alignment of specimens. Principal component analysis (PCA) was used on the Procrustes shape coordinates with the function gm. prcomp to find the major axes of shape variation within our sample. 3D wireframes were produced with plotRefToTarget to visualise shape modifications along PC axes with respect to the mean shape, obtained with mshape and shape. predictor functions. Data visualisation was performed with the package ggplot2 3.
3. 3 (Wickham, 2011). First, we ran a PCA on the total dataset to visualise the variation of sinus shape during growth in all our modern Crocodylia samples. To assess the global effect of size, phylogenetic groupings, skull shape and lifestyle on sinus shape, we performed Procrustes ANOVA on the Procrustes coordinates in the total ontogeny morphospace using the function procD. lm, which corresponds to a non-parametric Permutational Multivariate Analysis of Variance  (Anderson, 2001). To accurately compare ontogenetic trajectories between species, we used Procrustes ANOVA of total shape and PC scores against log transformed centroid size and species (as a factor). A homogeneity of slopes test was performed using the anova function on the   3. to visualise the axes of shape variation most correlated with size, using ggplot to assess ontogenetic changes in sinus shape.