dataesr/scientific-paragraphs-categorization huggingface dataset:
Code availability The IPSL-CM6A-LR model code used in this work was frozen (version 6. 1. 0) and subsequently altered only for correcting diagnostics or allowing further options and configurations. Versions 6. 1. 0 to 6. 1. 11 are therefore bit-reproducible for a given domain decomposition, compiling options and supercomputer. LMDZ, XIOS, NEMO and ORCHIDEE are released under the terms of the CeCILL licence. OASIS-MCT is released under the terms of the Lesser GNU General Public License (LGPL). IPSL-CM6A-LR code (version 6. 1. 0) is publicly available through Apache Subversion (svn) control system, with the following command lines under Linux: svn co http://forge. ipsl. jussieu. fr/igcmg/svn/modipsl/trunk modipsl; cd modipsl/util;. /model IPSLCM6. 1. 11-LR (IPSL-CM model development team, 2021).
In 2021, the values of GDD obtained on the day before the frost events in the concerned area vary in the range 150°C. day to 350°C. day, with an average value on 5 April of 259°C. day over the study domain. This value is high for this calendar day (rank=14th since 1921 in the E-OBS extended dataset) but the record value was obtained in 2020, with a mean GDD of 320°C. day. Given the range of values taken in the domain, we considered 3 thresholds for GDD: 250°C. day as a central value, and 150°C. day and 350°C. day as sensitivity experiments. This range of values also helps to capture a range of bud burst values of grapevine cultivars as found in Garcia de  Cortazar-Atauri et al. (2009). For each GDD threshold, the yearly minimum TN values (TNn), respectively called hereafter "TNnGDD250", "TNnGDD150" and "TNnGDD350" for the three GDD thresholds, is calculated over subsequent days and until the end of July at each grid point and then averaged over the domain.
day. This index is again calculated by grid point before being averaged spatially, or is taken at stations. Event attribution methods used in this study are well documented in previous studies. The rapid attribution methodology is a classical probabilistic approach, described in  Philip et al. , 2020 and van Oldenborgh et al. , 2021 , and has been used in many case studies for heat waves (e. g. Kew et al. , 2019 , Vautard et al. , 2020) , extreme precipitation (e. g. Philip et al. , 2018) , or more complex events such as wildfire weather  (van Oldenborgh et al. , 2020). It uses a stepwise approach analyzing observations with a Generalized Extreme Value (GEV) with a global warming index as a covariate, then using ensembles of models validated on the event indices and their extreme value statistics by comparison with observations, and finally using the GEV with the covariate fit to build a statistical model of the data under some assumptions.
Observations and past trends The observations used here for the attribution are the E-OBS v23e dataset of daily minimum temperatures. In Figure  3   For different thresholds we also find cooling trends, however with lower significance (Figure  3b ). The significance of the signal remains. Interestingly, over the last 50 years (1971-2020) the trends have increased and become more significant (for instance +0. 29°C/Decade, p<0. 1 for TNnApr-Jul, and -0. 37°C/Decade for TNnGDD250, p<0. 1; see also Figure  4 ). When considering trends in low extremes of these indices, the results are qualitatively similar but significance is increased when considering GEV fitting using the smoothed observed GMST as covariate instead of assuming a linear trend (see Table  1 ). We estimate that the event, defined as minimum temperatures over Apr-Jul, has a return period of 78 years [at least 19 years], which means a very rare event in the current climate.
3°C) but high GDD (321°C. day on 5 April); for Charnay-les-Mâcon: record frost (-4. 4°C, with 266°C. day on 5 April), and for Charmeil: the most severe frost among stations at our disposal (-6. 6°C with 244°C. day on 5 April). Detection results are shown in Table  2 , for these stations, and for the three main indices: TNnApr-Jul, TNnGDD250 and TNnGDD250-350. In almost all cases, the trends are positive for the fixed season index and negative for the growing season period. However, almost no result is statistically significant. We conclude that at local scale, variability is dominating trend signals ( Results here differ from the rapid attribution analysis  (Vautard et al. , 2021)  in the completion and adjustment of the E-OBS dataset by the producers. This led to slightly different values for the observed indices in 2021.
Model ensembles For the attribution of the frost event, we use five model ensembles. Each simulation of each ensemble was bias-adjusted using the CDFt method  (Vrac et al. , 2016)  using the daily minimum and the daily average temperatures from E-OBS over the 1981-2020 period. Bias correction is an important step here since GDD calculations use a threshold. This method was assessed for use in climate services in  Bartok et al. (2019) , and showed good performance. We used statistics of pooled ensembles, using data until 2021 for the GEV fit of the distributions. The fourth ensemble used is a set of 10 SST-forced HighResMIP simulations  (Haarsma et al. 2016). For the historical time period , the SST and sea ice forcings used are based on observed dataset, and for the future time period (2015-2050) the SST and sea ice are derived from CMIP5 RCP8.
Author contribution RV, GJVO and RB designed the experiments and the related analyses, advised by FO, NV, MR, and IGCA. SL, SK, SP, YR, RB, BD and JMS helped preparing the datasets. All authors contributed to the text. publication for a description and an assessment of this ensemble in the historical period. Daily mean and minimum temperatures were corrected at grid point level using the E-OBS observation dataset from 1981 to 2020. Bias correction follows the method described in  Vrac et al. (2016)  refined in  Bartok et a. (2019)  and applied on daily data instead of hourly data. The GCM-RCM ensemble is described in Table  A.
Acknowledgements We are grateful to  James Ciarlo  ( ICTP ) who made available in advance to publication on ESGF one of the regional climate simulations for the Euro-Cordex ensemble. The analysis benefitted from the collection of the Euro-Cordex simulation made within the  Copernicus Climate Change Service. The final analysis of this study is partly supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  101003469  ( XAIDA  project). It is among the case studies treated in this project. Our colleague co-author Geert Jan van Oldenborgh started the analysis with the author's team but sadly passed away in October 2021 before the article was submitted. The first author dedicates this study to his mother who passed away on the day before the frost event and who gave him her passion for flowers, nature and weather.
All the plants on the sampling area and the soil attached to roots (until 30 cm of depth) were collected in plastic bags and stored at -4 • C. Mycorrhizal analyses were carried out at the Tropical and Mediterranean Symbioses Laboratory (LSTM-CIRAD Baillarguet, France). Plant roots of each sample were cleaned from soil particles, and 3 replicates of 90 root fragments (1-cm long), collected from the central part of the root length, were analyzed in each variety and treatment. Root fragments were stained, observed under a microscope (Provis, Olympus), and rated according to a range of classes as described by the MO-M-C-04 protocol, according to Delteil et al. [50]. Data were processed through Mycocalc software  [51] , which provided the following parameters: (i) rate of root length with internal mycorrhizal structures (vesicles or hyphae) (ii) intensity of mycorrhizal colonization in root fragments, and (iii) abundance of arbuscules in the mycorrhizal parts of root fragments.
Yield Components Three days before harvest, plants of a sampling area (two wheat rows × 40 cm of length) were sampled in each plot for counting the number of plants, and tillers and spikes per plant, and that of grains per spike. After oven-drying (80 • C for 48 h), the shoot biomass (dry weight) was also determined. Harvest took place from 30 June to 6 July according to the considered year (Table  S1 ), using a mini-combine harvester. The grains of each plot were weighed and subjected to humidity determination. For each replicate/treatment, 3 samples were used for calculating the thousand-grain weight (TGW). Here the data of cv 1 only are presented, where all the sensors were placed.
Statistical Analysis Data on microclimatic and edaphic parameters, wheat yield and mycorrhizal colonization, measured in C, AF and AF+ treatments, were subjected to ANOVA using R studio software ver. 2. 7 [RStudio Public Benefit Corporation (PBC), Boston, MA, USA]. Separation of means was set at p ≤ 0. 05 with the Tukey's HSD test. Hourly data of air temperature and relative humidity from wheat sowing to harvest, in C and AF treatments, were classified as "day" or "night", according to the sunset and sunrise times of each day, thanks to the R package "RAtmosphere".
Funding: This study was undertaken in the frame of the  AGFORWARD  project (grant agreement no. 613520 ), co-funded by the  European Commission, Directorate-General for Research and Innovation , within the  7th Framework Program of RTD, Theme 2-Biotechnologies, Agriculture , and  Food. The research fellowship of A. P. allowing for the "writing-review and editing" of this Manuscript was granted by the  University of Padova   BIRD210158/21.
These numbers differed since it was not always possible to validate all the criteria presented in Fig. 2. For instance, sometimes it was not possible to see all the criteria required to validate the presence of a positive facial expression, because for example the lips were not visible on the video, and therefore their position was unclear. In that case, a positive facial expression could not be validated and we considered the data as missing. The two variables 'allogrooming' and 'positive facial expression' could be matched for 105 sessions of brush use. The statistical analyses presented in the results section were conducted on these respective numbers of sequences.
Plant Material Two self-incompatible and non-apomictic citrus varieties were used to conduct this study: 'Clemenules' Clementine, which is the main mandarin variety cultivated in Spain for its exceptional fruit quality; and 'Fortune' mandarin, which produces fruits with excellent organoleptical qualities and a very late harvesting period. These varieties are grown in plots in the Citrus Germplasm Bank of the Instituto Valenciano de Investigaciones Agrarias (IVIA) in Moncada, Valencia (Spain). They have the following accession numbers: 'Fortune' mandarin (IVIA-080) and 'Clemenules' clementine (IVIA-022). Tetraploid 'Clemenules' clementine was obtained by shoot tip grafting combined with colchicine treatment  [69]  and was grafted onto 'Carrizo' citrange (C. sinensis (L. ) Osbeck x Poncirus trifoliata (L.
Experimental Procedure Three independent self-pollination experiments were carried out under different conditions indicated below as Experiments 1, 2 and 3. Additionally, self-and cross-pollination under field conditions and cross-pollination in the temperature regimes studied in Experiment 1 were performed to confirm the normal behavior of pollen performance in the flowers utilized in our experiments. The average temperature under field conditions within the experimental time frame was 18. 5 • C, with a typically gradual increase from less than 10 • C at night to up to 30 • C in the daytime. The temperature data was acquired by an automatic weather station located at IVIA (Moncada, Valencia, Spain). To pollinate 'Fortune' mandarin flowers, we used the pollen of the 'Clemenules' clementine and vice versa.
Separation was carried out by capillary gel electrophoresis in a Genetic Analysis System 8000 (Beckman Coulter Inc. , Brea, CA, USA). PCR products were initially denatured at 90 • C for 2 min, injected at 2 kV for 30 s, and separated at 6 kV for 35 min. Alleles were size-based on a DNA size standard (400 bp). The GenomeLab v. 10. 0 (Beckman Coulter Inc. , Brea, CA, USA) genetic analysis software was used for data collection. The recovered plants were also genotyped with the SNP markers following the KASPar technology (LGC Genomics, Ipswich, UK). The KASPar Genotyping System is a competitive allele-specific dual Förster resonance energy transfer (FRET)-based assay for SNP genotyping. Primers were directly designed by the LGC Genomics Company based on the SNP locus-flanking sequence (∼50 nt on each side of the SNP).
Population Diversity Analysis The population diversity organization between the diploid and tetraploid plants recovered from the self-pollination of 'Fortune' mandarin and tetraploid 'Clemenules' clementine, respectively, was examined with the DARwin6 software  [80]. A neighborjoining analysis was performed using the simple matching dissimilarity index (d i-j ) between pairs of markers (units): d i-j = 1 - 1 L L ∑ l=1 m l π where d i-j is the dissimilarity between units i and j, L is the number of markers, m l is the number of matching alleles for marker l, and π is the ploidy level. From the obtained dissimilarity matrix, a weighted neighbor-joining tree  [81]  was computed.
Statistical Analysis All data are presented as the mean of the experimental values obtained in quadruplicate. The error values of the data shown in tables as numerical values or in graphs as error bars correspond to the standard error of the mean (SEM) calculated from the standard deviation of the values. One-way ANOVA was used to determine the differences in the AFB1 production and fungal growth data between the control and treated groups. A T-test was used to determine the significant difference between the means of two groups. Differences were considered statistically significant for p-values less than 0. 05. GraphPad Prism 9 software was used to perform all statistical analyzes.
Acknowledgements We thank the  Uganda Museum, Kampala  ( Rose Mwanja ,  Sarah Musalizi ,  Christopher Sebuyungo ) for access to fossil material in its care, and the  Uganda National Council for Science and Technology  for authorization to carry out research in the country. Pierre Mein measured the specimen from Moroto and made preliminary identifications. Hugo Salais  ( Metazoa Studio ) for the 3D reconstruction,  Agustín Ruella  for a previous version of the 3D reconstruction,  Marcela Tomeo  for the realisation of Figs 1 and3, and  Juana Yañez  for the reconstruction of the palaeoenvironment. Francisco Javier Ruiz-Sánchez  of  University of València  provided the use of the electron microscopy. Maria Dolores Marin-Monfort  for the taphonomical comments. We should thank  Frank Sénégas  for making the casts ( Sorbonne University ).
The diversity of red algae is significantly increasing; in the recent interest of climate changes, taxonomic and biogeographic studies are needed in untouched regions such as ecological important islands. We examined a collection of Gelidium specimens gathered during the 2010 Atimo Vatae expedition based on the morphology and DNA sequencing of mitochondrial cox1 and plastid rbcL. Both morphological and molecular datasets demonstrated the presence of three species in Madagascar;.
Materials and Methods Specimens collected in southern Madagascar were pressed onto herbarium sheets and subsamples were dehydrated in silica gel for molecular work; housed at the cryptogam herbarium of the Muséum National d'Histoire Naturelle in Paris, France (PC). Fragments of herbarium specimens of Gelidium sclerophyllum W. R. Taylor from Sri Lanka, Hawai'i, and Mexico were available from the Ghent University macroalgal herbarium, Ghent, Belgium (GENT; collection now housed in the herbarium of Meise Botanic Garden, BR), a personal herbarium at Sherwood Lab (accession number: ARS), University of Hawai'i at Mānoa, USA, and the Natural History Museum, Chungnam National University, Korea (CNUK). Information of samples used in the present study is given in Table  1. Morphology of specimens from Madagascar was examined under a light microscope.
Microscopic observations were made by sectioning with a razor and staining with 1% aqueous aniline blue acidified with 1% HCl and mounted in 70% glycerine. Photographs were taken with a DP-71 Olympus camera (Olympus, Tokyo, Japan) attached to a BX51 Olympus microscope (Olympus, Tokyo, Japan). DNA extraction, polymerase chain reaction amplification, and sequencing procedures followed Boo et al. [16]. The primers used for amplifying and sequencing were F7, F645, R753, and RrbcS start for plastid rbcL  [12, 34, 35] , and COXI43F and COXI1549R for mitochondrial cox1  [36]. Sequences of the forward and reverse strands were determined for all taxa, and the electropherograms were edited using MEGA version 7  [37]  and checked manually. Newly generated sequences were deposited in GenBank (Table  1 ).
Sequences were aligned using the MUSCLE algorithm in MEGA7 with default parameters and the alignment was manually adjusted. Four outgroup species in the family Gelidiaceae  [16, 38]  Phylogenies of plastid rbcL and mitochondrial cox1 datasets were reconstructed using maximum likelihood (ML) and Bayesian inference (BI). The best-fitting nucleotide substitution model was selected using Modeltest v3. 7  [39]  with Akaike Information Criteria (AIC). The ML analyses were performed using the Pthreads version of RAxML v8. 0. X  [40]  set as follows: a rapid bootstrap analysis and search for the best-scoring ML tree in one single run with 1000 bootstrap replicates under GTR+G+I model. The BI analyses were performed for individual datasets with MrBayes v3. 2. 1  [41]  using the Metropolis-coupled Markov Chain Monte Carlo (MC3) with the GTR+G+I model.
However, there are similar patterns between complex of cryptic species, with a certain degree of genetic divergence, such as Pterocladiella australafricanensis E. M. Tronchin and D. W. Freshwater, brown algae Ecklonia radiata (C. Agardh) J. Agardh and Lobophora crassa Z. Sun, P. E. Lim, and Kawai, and a green alga Codium duthieae P. C. Silva  [20, 21, 23, 67]. The range extension between the Southern and Northern Hemispheres in the Western Indian Ocean region might result from a southward dispersal from the Arabian Sea to southern Madagascar or vice versa. This hypothesis needs to be confirmed by additional sampling along the east coast of Africa. The restriction of Gelidium leptum to Madagascar is supported by a DNA database of global species of Gelidium. The endemism in Madagascar is not uncommon in species of the Gelidiales  [16, 24, 28, [30] [31] [32].
This gregarious species is an important pine defoliator and is known to readily respond to climate change with a consistent and sustained range expansion/shift since the 1990s, as winter warming facilitates its survival in previously unsuitable areas. First, we determined the thermal performance curve of development rate for each stage from the egg to the fourth larval instar by monitoring molting in larval colonies exposed to fluctuating thermal treatments in controlled conditions. Second, we developed a phenology model to simulate the cumulated development rate across successive life stages, using observation data of adult flights and daily mean temperatures as input variables. A good fit was found between predictions and observations. Finally, the model was used to explore phenological consequences of hypothetical climate variations.
Statistical Analyses The effect of thermal treatment on stage development rate was tested using Kruskal-Wallis tests. A Benjamini-Hochberg correction was used to deal with false discovery rate (FDR) associated with consecutive tests. All tests were performed using R version 3. 5. 0 (R Core Team, 2020). To define performance curves from the experiments. All calculations and analyses were done using R version 3. 5. 0 (R Core Team, 2020). For each thermal treatment, the mean date of stage changes were calculated per colony and the development time of each stage was considered as the difference between successive mean dates to then compute developmental rates. Subsequently, 37 non-linear equations from the literature [available in the DevRate R package  (Rebaudo et al. , 2018) ] were fitted to development rates to determine the best fit for each stage.
Model Validation Using Field Data The accuracy of the phenological model was estimated based on an independent dataset (i. e. , observation data not used for model construction). PPM phenology was monitored in Orléans (47. 947 • N, 1. 870 • E). Automatic pheromone traps (CapTrap) R were used to monitor daily male flights in summer 2019 (Supplementary Figure  1 ). Larval development was also monitored every 2 weeks in the field, from October 2019 to February 2020. Each time, 15 PPM nests were collected on Pinus nigra and the number of larvae per stage was determined in each nest from 60 randomly sampled larvae. This subsample size was determined after preliminary analyses in which all larvae inside the nests were counted, which showed that 60 larvae were enough to adequately assess the overall percentages without counting thousands of individuals per sampling date (Supplementary Protocol 1).
The observed percentages of individuals per stage at given dates were available from L2 to L5. No observation data was recorded on L1 because of the fast development of this stage and the difficult detection of young colonies in the field when foliage damage and silk weaving are not conspicuous yet. This monitoring was part of a broader field survey  (Poitou, 2021). Daily mean temperature in 2019-2020 was retrieved from the climatic station of Orléans (ID number 45234, 47. 827 • N, 1. 909 • E), which is part of the INRAE National Agroclimatic Network managed by the service unit AgroClim (Avignon, France) (Supplementary Figure  2 ). The percentage of individuals per stage that was observed in the field was compared to model predictions for all dates at which observation data was available (8 dates).
ACKNOWLEDGMENTS We greatly thank  Alexis Bernard ,  Louis Gross , and  Patrick Pineau  for their crucial technical assistance toward this work. We acknowledge the city of Saran for kindly allowing us to install traps, as well as the AgroClim unit from  INRAE  for providing daily temperature data. We are grateful to  François Rebaudo  for his help on the use of the DevRate package. This study was made possible with the financial support from the  Centre Val de Loire Region (LP's Ph. D. grant), the  French National Research Agency (ANR)  in the frame of the project called PHENEC (data collections, field samplings, equipments), and the  TEMPO French Network-tempo. pheno. fr-forproviding support which contributed to fund the automated monitoring equipment essential to this study.
Statistical analyses 2. 6. 1. Differences in phenolic contents in untreated vs. inoculated phloem We detected an extreme outlier (>5 deviations above the mean) in post-treatment phenolic concentration for one tree. Removing this outlier produced a dataset of 96 trees in total, including 25 dead, beetlekilled, and 71 surviving (Table  1 ). All statistical tests and analyses were based on this filtered dataset. Differences between median phenolic compound values measured in uninoculated and inoculated phloem (14 days after malt-agar inoculation), were tested using a paired two-sample Wilcoxon test, also known as Wilcoxon signed-rank test, a nonparametric alternative to paired T-test that allows comparisons among paired non-normally distributed data  (Woolson, 2008).
1). The influence of tree size (stem diameter) and spatial patterns of trees (distance between attacked trees) was also evaluated (see Appendix A for details). We determined the relative importance of each predictor based on their impact on a mean Gini coefficient for the model ensemble. Thus, Random Forest was used as a data reduction tool, enabling us to select and incorporate a small number of the most important survival predictors in a more formal, hypothesis-driven statistical framework  (Lucas, 2020). Analyses were performed using the "randomForest" package in R.