dataesr/scientific-paragraphs-categorization huggingface dataset:
22 IDENTITY CARD • Name • RFID • Date of tagging • Sex • … DETECTIONS • RFID • ID of the antenna • Date and time ANTENNAS • ID of the antenna • Location • Side (in or out) MANIPULATIONS • RFID • Name • Year MEASURES • RFID • Measure type • Value BREEDING ACTIVITIES • RFID • Year • Breeding status … Figure S1 : Schematic illustration of the main tables of the database. Each light grey box corresponds to one table. The database is built around the main table (IDENTITY CARD) containing all the key information of the individuals. The other tables refer to this main IDENTITY CARD table with the identity of the individuals given by the RFID numbers. For example, the DETECTIONS table records all RFID detections with the RFID identity of the detected individual and the identity of the antenna, also linked to ANTENNAS table compiling all information about the different antennas.
Supplement C Missing detection correction performances The algorithm to solve the missing detections was tested based on the detections from our ground truth dataset to assess its performances according to various degrees of missing detections. The first step was to get a cleaned detection dataset with corrected detections (because we used detections from the field). Thus, detections have been corrected with the algorithm to remove original missing detections and we manually validated the new corrected detections. Then, this cleaned dataset was used to test the algorithm performance by removing a various number of detections and by comparing the cleaned dataset with the newly corrected one (Figure  S4 ).
Supplement D Breeding date determination We trained a new CNN model (with the same architecture and hyperparameters as before) to classify whether the detection vector was aligned with the breeding date (positive class) or not (negative class). To build the positive class, a dataset where each vector was aligned to a known breeding date was designed. We used ground truth breeding cycles with a known breeding date and truncated the detection vectors around the breeding date (30 days before and 75 days after). These vectors constituted the positive class of our training dataset with vectors aligned on the breeding date. The dataset was completed with a negative class corresponding to breeding cycles that were not aligned to the breeding date (e. g. , starting at an unrealistic.
The resulting model's accuracy was then tested on a separate dataset of 1,164 breeding cycles, where breeding outcomes and sex were determined by human observation of breeding cycles (e. g. , as in Figure  S6 ). In this new application, no hyperparameter in the architecture of the model was changed (i. e. , we kept the same number of layers, filters, and kernel), but the length of the input vector (364,2) was modified to fit the shorter  The duration in or out of the specific area is given in days (d). The presence/absence patterns presented here correspond to the annual activities of a male king penguin but can be applied to any individual or any targeted species to understand, for instance, how long an individual stays or leaves a specific study area where RFID antennas are installed. Phases of the breeding cycle, identified from the presence/absence patterns of a bird, are indicated by the white dashed lines.
Acknowledgements This study was supported by the  Institut Polaire Français Paul-Emile Victor (IPEV)  within the framework of the  Program 137-ANTAVIA , by the  Centre Scientifique de Monaco  with additional support from the  LIA-647  and  RTPI-NUTRESS (CSM/CNRS-UNISTRA ), by the  Centre National de la Recherche Scientifique (CNRS)  through the  Programme Zone Atelier de Recherches sur l'Environnement Antarctique et Subantarctique (ZATA) , by the  Deutsche Forschungsgemeinschaft (DFG)  grants  ZI1525/3-1  and  ZI1525/7-1  in the framework of the priority program " Antarctic research with comparative investigations in Arctic ice areas ", and by  Academy of Finland  grant # 331320. This study was approved by the  French ethics  committee (last: APAFIS# 29338-2020070210516365 ) and the  French Polar Environmental Committee  and permits handling animals and access breeding sites were delivered by the " Terres Australes et Antarctiques Françaises " (TAAF).
We are deeply grateful to all the wintering and summering members of Program 137,  Denis Allemand ,  Benjamin Friess ,  Yvon Le Maho ,  Victor Planas-Bielsa ,  Claire Saraux , and all the other colleagues and students within the team who have contributed over the last 20 years to the improvement of hardware, software, and databases. We also sincerely thank the  IPEV logistics teams  for their important and continued support in the field.
3) Building the training dataset: direct observations of RFID-tagged individuals are used to build a ground truth dataset of labelled vectors giving the true classification. B) Model development. 4) Building and tuning the CNN models: the architecture of deep learning models and hyperparameters are tuned with the training dataset. Data augmentation is implemented to cover more biological and technical variance. An individual network is built for each classification problem (e. g. , breeding status, sex). 5) Post-processing: classification networks are derived to extract other biological information requiring a post-processing step such as location of stereotyped patterns in RFID data (e. g. , determination of the breeding dates with a probability curve (in blue) over presence/absence pattern in black and white, respectively).
6) Visualisation tools: models are validated and interpreted with visualisation tools (e. g. , with black curves representing the focus of the model during the breeding season). C) Model deployment. 7) Building the testing datasets: a testing step is used to remove biases induced during parameterisation with comparison of model classifications and manual techniques of data processing (i. e. , human expert classifications). 8) Testing the CNN models: model tests using expert-labelled datasets assess performance but also ensure that model performances are consistent according to classes and individual characteristics (e. g. , age, sex, life stage). 9) Application: classifications are applied to all detection data after pre-processing and formatting (i. e. , after correction of missing detections and building of vectors), and results are represented in Sphenotron for each individual (successful breeding cycles in green, failed breeding cycles in red).
1. two models to determine if an individual in a given year was a breeder (Breeding vs. Non-Breeding) 7 and if the breeding cycle was successful (Success vs. Failure), 2. a model to distinguish the sex of an individual through sex classification of only breeding cycles and a prediction compiling all the sexes identified over the lifetime breeding seasons, 3. a model to determine the most likely breeding date of males and females separately, through postprocessing of a CNN model. CNN architecture of these models was chosen using a classical simple architecture (seeLeCun et al. (2015)    and through trial and error (see details in S10). Each model was trained on a training set of 80% of the dataset, and the remaining 20% was used as a validation set to measure model performances and avoid overfitting (shown by low validation accuracy and high training accuracy), as suggested by Christin et al.
date): we draw a random breeding date for each correct breeding cycle and truncated the vectors around this random date, giving us the second half part of our training dataset. This data generation was repeated at each iteration (epoch) of the training to cover the maximum number of unrealistic breeding cycles possible while keeping a 50/50 ratio of positive to negative classes at each iteration. The data augmentation process were tailored to fit with this specific classification: the shift of breeding cycles were turned off because it would make our classification irrelevant. To apply this model and obtain the most probable breeding date for a given breeding cycle, we classified detection vectors that were aligned to each 12-hour period between November 1st to April 1st, and assessed the probability of having a correctly aligned vector with the previously trained model.
We chose 200 epochs(i. e. , training iterations)  for training of each model, which yielded the best results for validating model accuracy while avoiding overfitting. Each model took approximately 1 hour to train using a laptop computer with a CPU Intel Core i7-10750H (2. 60GHz) and a GPU Nvidia GeForce GTX 1660 Ti, a non-prohibitive technology as of 2023. The performance of models, according to the validation datasets used to select the CNN architecture and hyperparameters, reached near perfection for the three models, i. e. , Breeding vs. Non-Breeding, Success vs. Failure, and Male vs. Female, with global accuracy of 99. 1%, 99. 7%, and 100%, respectively. As expected, the three models without a data augmentation step achieved lower performances with global accuracy of 94. 6%, 91. 5%, and 96.
, 2018). We extended the mixture models of  Tiley et al. (2018)  to determine the ploidy of a sample with the model weights as a selection criterion (Supplementary Methods: Ploidy Test). PATÉ control files with all filtering options for GATK are provided in the Supplementary Data (Dryad X). To investigate nucleotide variation and the potential population genetic value of non-coding versus coding regions of target-enrichment loci, we identified the following regions: 1) Left Flank -Non-coding sequence upstream of the 5' start codon or splice site 2) Core -The coding region targeted by probes for sample enrichment 3) Right Flank -Non-coding sequence downstream of the 3' stop codon or splice site Identification of individual regions was automated (https://github. com/gtiley/Locus-Splitter) based on the tblasx program from BLAST v2.
10. 0  (Camacho et al. , 2009)  to find in-frame alignments between our assembled sequences and the original transcriptome loci used for probe development. Individual regions were aligned with MAFFT v7. 471  (Katoh & Standley, 2013). The aligned regions were then concatenated together to make whole-locus alignments. Because we are interested in investigating the analytic impact of ploidy in sequence data, we created three datasets from the phased ploidy-aware data: 1) One Haplotype (OH) -Only one phased haplotype sequence was randomly selected from each individual for each locus. 2) Ambiguous Genotype (AG) -The genotypic information for an individual is represented as a single sequence with IUPAC ambiguity codes for biallelic sites. Analyses that treat IUPAC codes as missing data were randomly resolved into two sequences.
Phylogeny estimation A species tree was estimated for all L. simplex individuals using whole-locus sequences for the OH, AG, and PH datasets with BPP v4. 6. 2  (Flouri et al. , 2020). We chose a full-likelihood multispecies coalescent (MSC;  Rannala & Yang, 2003)  model since it is robust to lowinformation gene tree estimates  (Xu & Yang, 2016). Markov chain Monte Carlo (MCMC) analyses collected 10,000 posterior samples, saving every 10 samples after a 10,000 generation burnin. Model and prior specifications are provided in the supplementary information (Supplementary Methods: BPP Prior Specification). The starting tree for our Bayesian species tree analyses was based on a concatenated maximum likelihood (ML) estimate from the OH data with IQTREE v2. 1. 2  (Minh et al. , 2020). We used unpartitioned analyses with ModelFinderPlus  (Kalyaanamoorthy et al.
Population structure Potential population structure within our sample of L. simplex from Madagascar was explored with STRUCTURE v2. 3. 4  (Pritchard et al. , 2000). For each dataset, we sampled only one biallelic SNP per locus, if present, and required a minimum minor allele count of three with no more than 25% missing data. To explore uncertainty due to sampling error of sites in a targetenrichment dataset that is relatively small compared to contemporary population genomic data, we generated 10 datasets with random SNP sampling. The number of clusters that best satisfy Hardy-Weinberg assumptions was determined with the 𝛥𝐾 method  (Evanno et al. , 2005). Principal component analyses (PCAs) were conducted on the OH, AG, and PH datasets with adegenet v2. 1. 3  (Jombart & Ahmed, 2011)  by compressing allele counts to the same dimension as the lowest ploidy level present.
For example, a hexaploid with three reference and three alternate alleles would be scored as a 2 on a scale from 0 to 4, to be comparable to tetraploids in the dataset. Additional details on allelic imbalances would be lost as, for example, hexaploids with either one or two alternate alleles would be represented as a 1 on the tetraploid scale. Scripts for generating PCA or other clustering-ready matrices from fasta files are available on GPT's GitHub (https://github. com/gtiley/fasta-conversion). Additional details about compression of PCA data matrices can be found in the Supplementary Material (Supplementary Methods: PCA Input Data). The presence of isolation-by-distance as an explanation for observed patterns of genetic structure and ordination was tested with the patristic distances from the ML analyses or the uncorrected pairwise distances (p-distances) regressed against the natural logarithm of the great circle distance between individuals.
Population genetic summary statistics The pairwise nucleotide diversity (𝜋), number of segregating sites (S), and Tajima's D were calculated across locus regions and datasets with the PopGenome v2. 7. 5 R package  (Pfeifer et al. , 2014). The significance of Tajima's D statistics was determined by 100 standard coalescent simulations with ms  (Hudson, 2002). To estimate 95% confidence intervals on summary statistics, we generated 200 bootstrap replicates of each alignment. The relative estimation error between regions and datasets was compared using the root squared mean error (RSME) from the bootstrap sample. The scaling and interpretation of parameters are described in the supplementary material (Supplementary Methods: Summary Statistics). We tested for differences in the distribution of summary statistics among locus regions (left flanks, cores, and right flanks) and data types (OH, AG, and PH) with Wilcoxon Rank-Sum tests.
, 2014)  and constructed a vague prior around this mean rate, such that any biologically plausible uncertainty in the rate of evolution is reflected in our posterior sample. It is a tenuous rate estimate at best, since it remains unknown whether polyploid L. simplex largely propagates clonally, through apomixis, or through sexual reproduction. The MCMC analyses for the OH, AG, and PH data collected 20,000 samples after a burnin of 50,000,000 and sample interval of 10,000. Two replicates were run for each analysis to evaluate convergence and mixing was assessed with Tracer v1. 7  (Rambaut et al. , 2017). All model and prior specifications are provided in the supplementary information (Supplementary Methods: EBSP Prior Specification) and are replicable from XML files on Dryad  (X).
Although available evidence leaves the ploidy of some individuals ambiguous and raises skepticism of the inference methods, genotyping and phasing of individuals proceeded with the ploidy suggested by our mixture models. We inferred an average of 247 loci with allelic variation, which had a heterozygosity of 0. 012 variants per base. After reducing the number of loci with BLAST clustering to avoid tight linkage, a final 301 loci were retained from the OH, AG, and PH datasets for downstream analyses.
Population sub-structure in Madagascar STRUCTURE analyses recovered a variable number of Hardy-Weinberg groups across the ten datasets that randomly sampled one non-singleton biallelic SNP per locus (Supplementary Table  S4 ). For the OH data, we found a K of two, three, or four in eight, one, and one of the respective replicates. For AG data, the optimal number of clusters was more ambiguous with a K of two, three, and four recovered for four, five, and one replicates. PH data found K of two and three for eight and two replicates. We note that the method of  Evanno et al. (2005)  cannot infer if one panmictic population is the best model, and while there is reasonable geographic structure at K of two weighted across biological and technical replicates (Fig. 2a ) the non-zero admixture between individuals at the most extreme ends could indicate a single population with a cline of genetic variation.
Variation in summary statistics There were significant differences in the distributions of 𝜋, S, and Tajima's D among locus regions and between the OH, AG, and PH data (Fig. 4 ). For 𝜋, there was always a difference between flanking regions and the exon core (Supplementary Table  S5 ), with the cores surprisingly having more variation. Differences were also observed between the flanking regions and cores for S and Tajima's D, except when comparing the left flanks and cores for OH data (Supplementary Table  S5 ). The only case of a difference between the left and right flanks was when estimating 𝜋 for AG data (Supplementary Table  S5 ). The OH and PH data resulted in similar estimates of 𝜋; however, the AG and PH data had similar distributions of S (Supplementary Table  S5 ). Any differences in Tajima's D statistics were largely explained by an over-representation of values less than negative two, indicating positive selection or population size expansion, in the OH data and among core regions across datasets (Table  1 ).
These limitations do not necessarily disagree with an assessment of EBSP models with a larger genomic dataset from endemic palm species in Madagascar  (Helmstetter et al. , 2021). Helmstetter et al. (2021)  found that the estimated Ne trends were valuable predictors of IUCN risk assessments, and that population decreases could be detected at anthropogenic time scales. However, it should be easier to detect population declines than population increases in a system that already had a large degree of standing genetic variation, such as L. simplex. This is because the time until the most recent common ancestor of a genealogy will be much more recent in a small population than a large one (e. g. see equation 2. 13 from Gillespie 2004). Analyses of demographic models with genomic SFS should be able to increase resolution at recent anthropogenic time scales  (Patton et al.
Acknowledgements GPT was supported by a  Marie Curie Individual Fellowship  ( MSCA-IF-EF-ST 101026923 ). GB is supported by  LabEx TULIP  ( ANR-10-LABX-0041 ). CERL was supported by a  The Royal Society International Collaboration Award. Thank you to  MEDD Madagascar ,  Madagascar National Parks ,  Isalo MNP , and  Itremo NPA  for supporting our research and exportation permits in 2014 and 2017, and also to  COSTECH Tanzania  for their support for the 2014 collection permits for MSV and WRQL. This work would not have been possible without  Stuart Cable  and everyone at the  Kew Madagascar Conservation Centre. We thank  Emily Lockwood  for DNA extractions, and  Lorena Endara  and  J. Gordon Burleigh  for coordinating library preparation and sequencing.
Data Availability Statement Scripts for analyses and input files are available through the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. [NNNN]. Additional scripts are available on GPT's GitHub for identifying exon-intron boundaries from target enrichment data (https://github. com/gtiley/Locus-Splitter) generating matrices for PCAs from fasta files (https://github. com/gtiley/fastaconversion) and the ploidy test implemented through PATÉ (https://github. com/gtiley/Phasing). Raw sequence data is available via the NCBI short read archive (SRA) database, with individual SRA and BioSample identifiers in Supplementary Table  S1. Raw sequence data is associated with NCBI BioProject PRJNA952819.
, 2011). Other examples of global dual-state CO 2 inversion systems include the Tan-Tracker system  (Tian et al. , 2014)  and a system based on the local ensemble transform Kalman filter (Y. Liu et al. , 2019 ). At regional scale,  Peng et al. (2015)  and  Kou et al. (2017)  tested two systems based on the offline Community Multiscale Air Quality (CMAQ) transport model, both at a horizontal resolution of 64 km × 64 km. There is a need to further test and develop dual-state CO 2 inversion algorithms, especially using online atmospheric transport models, which would allow for higher-resolution simulations of the atmospheric CO 2 transport. This paper presents the TRACE Regional Atmosphere-Carbon Ensemble (TRACE) system, a regional coupled atmosphere-carbon data assimilation system for CO 2 flux inversion.
Atmospheric and Flux Data The regional atmospheric model was driven by meteorological initial and boundary conditions from the ERA-Interim reanalysis  (Dee et al. , 2011). The ERA-Interim data had a spatial resolution of about 81 km, 60 vertical levels, and 6-hourly time resolution. Initial and lateral boundary conditions in CO 2 mole fractions were taken from the global CarbonTracker CT2019B data set  (Jacobson et al. , 2020) , which has a spatial resolution of 3° × 2° longitude-latitude and 25 vertical levels. The initial conditions in atmospheric CO 2 mole frac tions were created by first initializing WRF-Chem with mole fractions from CT2019B (linearly interpolated to the WRF-Chem grid), and then running the model for two months to allow the mesoscale model to simulate high-resolution gradients in atmospheric CO 2.
For the last three ALT experiments, we used alternative datasets for the biogenic and oceanic fluxes (see Section 3. 1) in the truth simulation, reflecting a more realistic scenario where the true flux error statistics are unknown. The inversions used the same prior fluxes as before. Three inversions with different prior flux param- The data assimilation experiments were started on 3 June 2016 at 0000 UTC to allow the system to spin up for 4 weeks. First, we ran the atmospheric model for 2 weeks without assimilating observations to establish the initial sensitivities of atmospheric CO 2 mole fractions to the flux parameters. Next, in situ CO 2 observations were assimilated to jointly constrain the atmospheric CO 2 mole fractions and CO 2 flux parameters. The first 2 weeks of assimilation were discarded to account for spin-up.
Inversions With Unknown Prior Flux Error Statistics In the previous experiments, the true flux parameters and ensemble perturbation parameters were sampled from the same error covariance matrix, and with a sufficiently large ensemble the truth would lie in the subspace spanned by the ensemble members. In reality, the joint distribution of the true flux errors are unknown and largely uncertain. A new set of experiments were therefore carried out to test a more realistic scenario where the true flux errors and their statistics were not known a priori. The prior fluxes in these experiments were the same as in the previous experiments, while the true fluxes were obtained from different datasets than the prior fluxes. that inversions using this type of prior flux parameter error covariances require a larger covariance inflation for the flux parameters.
Summary and Conclusions This study introduced the TRACE Regional Atmosphere-Carbon Ensemble (TRACE) data assimilation system and demonstrated its CO 2 flux inversion capabilities in a series of controlled experiments. The main novelty of TRACE is its ensemble-based simultaneous state and parameter estimation (ESSPE) approach, which opens up possibilities to assimilate dense high-resolution observations from for example, spaceborne CO 2 observations. Here we focused on rigorously testing the system using synthetic observations from the conventional CO 2 tower network. Some error sources were deliberately excluded to simplify the interpretation of the results. As a consequence, the results are overly optimistic, and should be considered as a demonstration of the capabilities of the TRACE framework, and not as an assessment of the observational network.