dataesr/scientific-paragraphs-categorization huggingface dataset:
1. The ability to generate seeds with only autonomous selfing was recorded for all hybrid individuals as a categorical factor. The hybrid individuals were classified into three categories based on the total number of seeds: 1) having almost no seed (< 10 seeds), 2) having few seeds (10-300 seeds), and 3) having plenty of seeds (> 300 seeds). As the hybrid individuals were not strictly separated in the growth chamber during flowering, the cutoff of ten seeds was applied to reduce noise from occasional pollen contamination from other Capsella plants. The seed set data of two individuals were removed from the dataset, because they were severely affected by disease during flowering time. The first group of RNA-seq data was from  Duan et al. (2023). Total RNA was extracted from the inflorescence of 6 diploid hybrids and 14 allotetraploids, using a cetyl-trimethyl-ammonium-bromide (CTAB) based method.
Determination of S-locus genotype and phenotype in the Capsella hybrids and synthetic polyploids and confrontation with mating system phenotype assessments In Brassicaceae, the self-incompatibility phenotype in pollen depends on complex dominance relationships among S-alleles, achieved through modifier genetic elements consisting in small RNAs encoded by precursors lying at the S-locus of dominant alleles and targeting the SCR gene of recessive alleles  (Tarutani et al. 2010; Durand et al. 2014). Dominance in pollen is thus regulated at the transcriptional level, and is associated with very strong inhibition of mRNA production of recessive alleles  (Burghgraeve et al. 2020) , which could potentially be revealed by analyzing RNA-seq data. Hence, we tested this approach by performing S-allele typing with the NGSgenotyp pipeline using separately a SRK reference database, to determine the S-locus genotype of individuals (because SRK alleles are always co-expressed in the style,  Hatakeyama et al.
2001) , and a SCR reference database, to determine which Sallele is majoritarily expressed in pollen (and thus putatively dominant). We applied this approach to RNA-seq data from 8 diploid individuals (7 C. grandiflora + 1 C. orientalis) and 7 tetraploids (6 C. grandiflora + 1 C. orientalis) used as parents in the hybrid experiments  (Duan et al. 2023) , with the same SRK reference database as above enlarged with newly obtained C. grandiflora allele sequences, and for SCR with a reference database of sequences from A. halleri and A. lyrata (Genbank sequences). For SCR, because of the higher sequence divergence among S-alleles than for SRK, we modified the NGSgenotyp parameters by reducing the k-mer size used for filtering to a value of 15 (the default size used for SRK was 20). This allowed us, with the de novo assembly module of NGSgenotyp, to obtain full coding sequences of SCR for all C.
Acknowledgements The work on SI in the Lille group is supported by the  Agence Nationale de la Recherche  ( TE-MoMa  project, grant number  ANR-18-CE02-0020-01 ), the  Région Hauts-de-France  and the  Ministère de l'Enseignement Supérieur et de la Recherche (CPER Climibio and CPER Ecrin  grants), and the  European Fund for Regional Economic Development. The study in Uppsala was supported by grant  2019-00806  from the  Swedish Research Council  to ML and  Nilsson- Ehle research grant from the  Royal Physiographic Society in Lund  to TD. ML also acknowledges support from the  Erik Philip-Sörensen Foundation. TD was supported by the  Sven and Lilly Lawski Foundation. The computation and data handling were provided by the  Swedish National Infrastructure for Computing (SNIC)  at Uppmax, partially funded by the  Swedish Research Council  through grant agreement no  2018-05973.
Data availability The RNA-sequencing data of the additional Capsella hybrids generated by this article are available in Sequence Read Archive (SRA) of National Center for Biotechnology Information (NCBI), and can be accessed with BioProject number PRJNA946929. All new Capsella SRK and SCR sequences obtained by de novo assembly with the NGSgenotyp pipeline are posted at https://www. doi. org/10. 6084/m9. figshare. 22567558.
on publicly available short-read resequencing data of 180 C. grandiflora individuals from Monodendri, Greece (the Cg-9 population in Josephs et al. 2015) , starting from a database of SRK sequences from A. lyrata and A. halleri and 62 partial C. grandiflora SRK sequences previously obtained by Sanger sequencing by Jesper Bechsgaard and Mikkel Schierup.
Among the benchmark methods based on this idea, one can mention the Break detection For Additive Seasonal Trends (BFAST)  [30]  and the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr)  [31]  approaches. More recently, the FORDEAD package  [32]  has been developed to detect bark beetle outbreaks in spruce trees. One drawback of such approaches is that they require historical data to properly define what is normal behavior. Moreover, Stahl et al. [18]  has pointed out that such an approach can struggle to detect "diffuse" disturbances (i. e. , subtle changes in spectral reflectance), which is typically the case with drought-induced dieback of oak trees. Supervised classification approaches can be preferred when the phenomenon under study is subtle and difficult to model  [17].
B. Reference Data 1) Labeling Protocol: The health status of the reference data is assessed using the DEPERIS protocol  [38]. This protocol is Fig. 1. Extent of the studied area is delimited by the gray area (the boundaries between the 11 tiles is highlighted in lighter gray). The frontier of the region Centre-Val de Loire and its departments is in white. Finally, the colored dots locate the reference data, with each color representing a labeling year (for reference data labeled multiple times, the last labeling year is highlighted). The background uses cloudless S2 images. used by the French Forest Health Service and is currently the official protocol for forest dieback monitoring in France  [39]. The DEPERIS protocol assesses the health status of individual trees by combining the percentages of dead branches and missing ramifications.
Finally, considering the entire dataset, the proportions of healthy, declining and very declining plots are 55. 77%, 24. 95%, and 19. 28%, respectively. Among the reference data, four forest massifs were labeled several years in a row (the number of labeled plots may vary slightly depending on the year and the forestry interventions). The Fontainebleau massif (∼55 plots) was labeled between 2017 and 2021. Orléans (∼25 plots), Vierzon (∼27 plots), and Marcénat (9 plots) forests were labeled between 2019 and 2022. Fig. 3  gives the percentage of healthy plots [see Fig. 3(a) ] and very declining plots [see Fig. 3(b) ] for each massif over time. Looking at this figure, there is evidence of a general increase in dieback. Moreover, the proportion of plots in severe decline is also increasing over time for all these massifs.
III. METHODS The proposed approach for multiyear forest dieback mapping is summarized in Fig. 4. The remainder of this section details each of the methodological steps of this diagram. Note that the algorithm operates at the pixel level. Therefore, when the model is trained, all pixels in a plot receive the plot label. This increase the variability of the training set, see Section V for a discussion and perspectives on this point. This extraction is done using the iota 2 software  [42] , which is a processing chain for the operational production of land cover maps from remote sensing image time series. A linear interpolation (i. e. , gapfilling) is performed to reconstruct missing data caused by clouds. Such approach is commonly used in remote sensing and have been applied with success in many application  [43] ,  [44] ,  [45].
The CRswir was successfully used for the mapping of bark beetle outbreaks in the FORDEAD package  [32] ,  [48]. CR aims at isolating individual absorption of interest  [49] , and has been mainly used with hyperspectral remote sensing image  [50]. Based on this idea, we propose a similar indicator (CRre) that focuses on the RE part of the S2 spectrum. Potential interests of such an indicator compared to normalized indices such as NDVI are 1) the fact that they combine more than two spectral bands and 2) they are not normalized and therefore not subject to saturation effects. They are also very easy and fast to compute when compared to more complex indices. A graphical illustration of CRre and CRswir is given in Fig. 6. It can be seen that both indices measure the absorbance of a specific band (B5 for CRre and B11 for CRswir) with respect to its local "convex hull" (B4 and B6 for CRre and B8A and B12 for CRswir).
C. Expanded Training References From Unlabeled Time Series Slices Accessing more training data, when possible, is a common practice to improve the accuracy of ML models  [57]. In our case, it seems intuitive that a broader and more consistent characterization of oak dieback patterns over time would be possible with a greater number of examples across years and massifs, especially in areas that are not consistently marked each year. We propose to expand our training dataset with unlabeled time series slices from plots that are only labeled for a specific year (denoted Y lab ), such as plots labeled in 2020 but without labels for the other years. To avoid adding noisy or misleading examples, a two-condition procedure is used to select the time series slices for which we have high confidence in the label to be assigned: 2) A declining plot (percentage of declining trees higher than 20%) labeled year Y lab was likely to be declining the years Y lab + 1 and Y lab + 2 and is added to the dataset with its correspond time slice (Y lab + 1, Y lab + N years ) and (Y lab + 2, Y lab + N years + 1).
These two rules are based on the reasonable assumption that forest recovery from dieback is relatively slow, i. e. , a declining plot cannot become healthy within 2 years (which is consistent with our reference data labeled several years in a row, as well as other studies, see for example,  [58] ). It is also assumed that there was no silvicultural intervention during this period, or at least that it concerns a limited amount of pixels. In Section IV, the interest of this data expansion step is evaluated w. r. t. using only the raw reference data. In Section V, we discuss on possible improvement of this procedure. Finally, note that this additional examples are never used to validate the performances of the models, they are only used as additional examples to train the model. Moreover, the plots already labeled the years Y lab -2, Y lab -1, Y lab , Y lab + 1, and Y lab + 2 are obviously not duplicated in the dataset to avoid data leakage.
D. Data Balancing Depending on the year, the different classes to be classified are not evenly distributed, which is typically known as an imbalance problem  [59]. We propose to balance the dataset each year independently ( i. e. , for each labeling year, we have the same number of samples coming from the three different classes) using the Synthetic Minority Oversampling Technique (SMOTE), which has been used successfully in many applications  [60]. In short, the SMOTE technique generates synthetic data based on the similarity in feature space between existing minority instances. The main advantage of oversampling techniques over undersampling techniques is that all available examples are kept in the training set. Other variants of the SMOTE algorithm were tested without improvement in our results (see Section V).
OA provides the percentage of correctly classified items, while F1 score is the harmonic mean of precision (percentage of samples correctly labeled in class j) and recall (percentage of samples of class j that were correctly labeled). BA is defined as the average of the recalls obtained for each class (  [66] ). Unlike OA, BA is not affected by imbalanced datasets, since the recall is expressed as a percentage for each class. Therefore, OA can be biased toward the majority class and thus be a misleading performance measure for imbalanced datasets  [68] , which is typically the case in our study. In this respect, our analysis emphasize the results expressed in BA.
G. Feature Importance Analysis With Random Forest The RF algorithm has the advantage of natively providing feature importance, which is computed as the (normalized) total reduction in Gini impurity introduced by a feature. Note that as a complement, we also used the Python library SHAP (version 0. 41. 0) with the Tree Explainer method proposed by Lundberg et al. [70] , which leads to similar conclusions. For the sake of simplicity, we do not present detailed results here, but it is interesting to note that both analyses agree.
To illustrate this point, Fig. 16  provides the median CRswir and CRre time series of the healthy pixels labeled in the Fontainebleau forest grouped by labeling year (we chose this forest because we the same area visited over time, allowing us to mitigate variations due to spatial location). We can clearly see that the inflexion point in April/May can vary over the years, e. g. , the year 2020 was advanced while the year 2021 was delayed. Without a well-balanced dataset, such year-specific differences can be used by the model to "learn" that a given year is (on average) declining (e. g. , 2021 with many declining references) or healthy (e. g. , 2020 with a majority of healthy plots). However, we also found that adding dates other than summer (June/September) improve the classification results, which means that there is interesting information outside the summer period that can be used to detect forest dieback, but that this information can be misleading if not used correctly.
C. Classification and Preprocessing Methods Regarding the classification method to be used, we found that the RF algorithm is well suited and provides good results without requiring intensive parameter tuning (note that during our preliminary analysis, the SVM algorithm performed similarly than RF, but its scalability to large datasets limits its usefulness for operational services). The fact that all models tested so far converged to similar results is encouraging (see Appendix A-B). Since interesting results have been obtained using standard deep learning approaches, further research on that point will be an interesting perspective. Indeed, an advantage of using deep learning approach could also be the potential   application to other types of trees via transfer learning  [79] , e. g. , Pinus sylvestris is another key essence of the studied area.
First, data expansion can be performed using time slices of unlabeled time series, exploiting the fact that healthy pixels were likely healthy in the years prior to labeling, and declining pixels are likely to continue declining in the coming years. Second, the minority class(es) can be oversampled to balance the final dataset. This highlights the importance of collecting samples over multiple years to capture the diversity in phenology. These results open up interesting perspectives, some of which are discussed in Section V. First of all, specific work on the classification model could improve dieback detection, especially using deep learning models. Since the reference expansion and balancing steps were found to be important for better generalization, further work on this part could increase model stability and performance.
C. Forecasting Study To analyze the potential interest of the proposed model for forecasting (i. e. , predicting dieback in future years) and to test its robustness to lack of data, an experiment was conducted by testing the model on a given year after training on the other years. Subset of reference data (for a given year) were selected by repeating ten times a stratified 5-fold CV. Average BA (2 classes) are reported in Fig. 17  with and without expanded references. One can see a decrease in the accuracy, i. e. , average BA over years is equal to 0. 721 instead of 0. 792 (see Table  IV ). This decrease was expected because the model is not calibrated to the year to be predicted and because a large number of samples are removed from the training dataset (this is especially true for 2020, where the number of training samples is divided by two).
ACKNOWLEDGMENT The authors would like to thank the two anonymous reviewers for their thoughtful and constructive feedback. They also thank  A. Vincent ,  H. Touchais ,  M. Fauvel , and  J. Inglada  for their support and help with the iota 2 software. They thank the  CNES  for providing us access to its high performance computing (HPC) infrastructure to run the map generation presented in this article. Finally, they would like to thank all the participants of the RECONFORT project, who provided reference data and feedbacks. More precisely:  Office National des Forêts (ONF) ,.
Acknowledgments We thank  Manos Mavrakis ,  Sophie Brasselet ,  Raphael Clément ,  Martine Ben Amar ,  Richard Smith ,  Adam Runion  for fruitful discussions on this project. We thank  Frédéric Galland  for advice on image analysis. We thank  Yohans Bellaiche ,  Jérome Solon ,  Manos Mavrakis ,  Claudio Collinet  and  Parisa Kakanj  for sharing stocks. This work was funded by the following agencies:  Agence Nationale de la Recherche  ( ANR-18-CE13-028 ,  ANR-17-CE30-0007 ) ;  Excellence Initiative of Aix-Marseille University -A*Midex  (capostromex), a  French Investissements d'Avenir programme ; The project leading to this publication has received funding from the  ≪ Programme d'Investissements d'Avenir ≫ of the French Government , managed by the  French National Research Agency  ( ANR-16-CONV-0001 ,  ANR21-ESRE-0002 ), and from  Excellence Initiative of Aix-Marseille University  -A*MIDEX.
Unfortunately, linking results from these studies to viral dynamics is not straightforward. Challenge and transmission experiments in which live virus is inoculated artificially into chickens, and eventually transmitted onwards  15, 32  , allow to estimate important properties of AIV epidemiology. However, because these experiments are con-ducted within a controlled environment, it remains difficult to draw general conclusions about AIV transmission in LBMs. Here we aimed to fill these gaps by modelling H9N2 AIV transmission in an LBM. Mathematical modelling has proven useful to study AIV transmission dynamics in LBMs, but such investigations have been mostly theoretical so far  22. Our work is instead grounded on a longitudinal dataset of H9N2 AIV acquisition in exotic and indigenous chickens in an LBM in Chattogram, Bangladesh  33.
Discussion In this work we characterised H9N2 transmission patterns in a single LBM in Bangladesh by fitting a mechanistic transmission model to a longitudinal dataset collected in the context of a field experiment. Our results confirm the important role of LBMs as hotspots of AIV transmission. We found high prevalence of H9N2 AIV, in agreement with previous studies and LBM surveillance in Bangladesh  15, 16. Our simulations further sug-gest that H9N2 AIV prevalence varies considerably during a single day due to high transmission rates. Such an effect has been illustrated in previous modelling work  22  , and should be accounted for by AIV surveillance initiatives and in the design of chicken sampling strategies in general. From a systemic perspective, high persistence and prevalence of H9N2 AIV in LBMs are concerning for the whole poultry production and distribution infrastructure in which LBMs are embedded.
Acknowledgements The authors would like to thank the  French Embassy in Côte d'Ivoire  for the award of a PhD scholarship and  Professor Koudou G. Benjamin , Director of  Research and Development at CSRS , for his nancial and material support for this project. The authors also thank  Dr Any Georges Moyabi  for his technical assistance in the cultivation of L. alba. We thank the  Vectopole platform (IRD, Montpellier)  and  Bethsabee Scheid  for providing technical support. The Vectopole is a platform of the Vectopole Sud Network (http://www. vectopole-sud. fr/) and is part of the  LabEx CeMEB  ( ANR-10-LABX-04-01 ).
In recent decades, field and semi-field studies of malaria transmission have gathered geographic-specific information about mosquito ecology, behaviour and their sensitivity to interventions. Mathematical models of malaria transmission can incorporate such data to infer the likely impact of vector control interventions and hence guide malaria control strategies in various geographies. To facilitate this process and make model predictions of intervention impact available for different geographical regions, we developed AnophelesModel. AnophelesModel is an online, open-access, R package that directly allows incorporating generated entomological data for adjustment of models to assess intervention scenarios according to species and location-specific characteristics. In addition, it includes a previously published, comprehensive, curated database of field entomological data from over 50 Anopheles species, field data on mosquito and human behaviour, and on estimates of vector control effectiveness.
Mosquito bionomics data The feeding cycle model relies on quantified ecological and bionomic characteristics of the mosquitoes, including the parous rate, the human blood index, the sac rate, their endophily and endophagy. AnophelesModel allows the user to input their own data and tailor the entomological model to the vector species of interest. Additionally, it also harbours an extensive database of relevant parameters collated from published literature and publicly available sources. Using a Bayesian hierarchical model applied to previously-published entomological data  (30, 37, 38) , mosquito bionomic parameters were derived for 57 Anopheles species and 17 complexes (groupings of sibling species) and included in the package.  Deterrency: the reduction in the availability rate of humans to mosquitoes per day, estimated based on the proportion of mosquitoes that fail to reach a protected human or are deterred from biting due to intervention  Pre-prandial killing: the proportion of mosquitoes that are killed before feeding  Post-prandial killing: the proportion of mosquitoes that are killed after feeding.
Modelled effects of LLINs included in the package A previously published system of logistic regression models (9, 30) can be used with the package to estimate the effects of LLINs deployments (cf. Supplementary Material). The decay of physical properties of mosquito nets in terms of attrition, use, physical and chemical integrity has been estimated using the data from the President Malaria Initiative (PMI) net durability studies  (39) , and on data from Morgan et al. (40)  as described in Briet et al. (9)  (cf. Supplementary Material). These datasets, containing properties of various net types in different countries, are also included in the package.
Entomological characteristics are defined through an entomology XML snippet and intervention effects can be defined through the "generic vector intervention" (GVI) XML snippet (further information about OpenMalaria XML definitions is provided at https://github. com/SwissTPH/openmalaria/wiki). The GVI snippet includes the definition of decay and initial effect parameters for deterrency, pre-and post-prandial killing effects of interventions. In OpenMalaria, the intervention effects modelled through GVI components can be associated one of seven possible decay functions. AnophelesModel uses nonlinear least squares (R package minpack. lm version 1. 2-2) to fit in turn each of the seven decay functions to the time series of estimated intervention effects and chooses the decay with the best fit (smallest residual sum of squares).