dataesr/scientific-paragraphs-categorization huggingface dataset:
These observations pave the way for practitioners that are invited to 1/ monitor calibration as well as accuracy, 2/ use the Average Logit method and 3/ estimate the optimal temperature on their own test dataset and use it for the model deployment. Differences in models' performance can be partly explained by model size. Indeed we found that models with the highest number of parameters (ConvNext, ViT, SwinTransformer) gave the best accuracy and ECE values. On the other hand, the only lightweight model, MobileNet, was consistently the worst model. Despite some literature showing that neural networks can be poorly calibrated, our result shows that this is not always the case (see also  Minderer et al. (2021) ), and that certain families of model architectures, such as ConvNext here, are intrinsically better calibrated than others, independently of the size of the model.
The calibration of each model can be further improved on each dataset using temperature scaling as post-processing function. However, determining the optimal T requires annotating at least a fraction of the target set of images, which is something that practitioners would like to avoid if possible. Fortunately, we showed empirically with three very different datasets that the optimal temperatures are very close from one dataset to another, which suggests the generalizability of a single temperature that can be determined and fixed for future test sets. That said, we do not claim that the optimal temperatures defined in this paper can be used directly when using one of the studied architectures. Indeed, these temperatures are valid for a given training procedure (datasets, hyperparameters).
In practice, it is mandatory to estimate the temperature using available test dataset(s) and subsequently maintain this temperature for deployment (since we showed it will be generalizable). This way, when the model will be used to classify new unseen data, the previously estimated temperature will ensure a better calibration of the predicted scores. Proper model calibration at the image level is not always sufficient, as many software programs and scientific studies operate at the scale of the sequences that define the relevant 'observations' or 'events' from an ecological viewpoint. It is therefore extremely important to be able to calibrate the predictions at sequence level. For the first time, we showed that the most intuitive approach, in which scores are averaged, did not provide the best accuracy and had the worst calibration, with largely under-confident predictions.
Data availability statement The five trained models, all derived data used in the analysis, and the code for the inference and metric calculation are available at https://doi. org/10. 5281/zenodo. 10014376. The Portugal and Alps datasets are available at https://doi. org/10. 15468/rah33j and https://doi. org/10. 5281/zenodo. 10014376. The Pyrenees dataset is available upon request only, because of the presence of a sensitive species (brown bear).
used 5 established machine learning architectures: EfficientNetV2, ConvNext, ViT, Swin Transformer V2, and MobileNetV3. (Tan and Le 2021; Zhuang Liu et al. 2022; Dosovitskiy et al. 2021; Ze Liu et al. 2022; Howard et al. 2019 ). We have selected these architectures to represent CNNs (EfficientNetV2, ConvNext), Transformers (Swin, ViT), as well as lightweight architectures that could be deployed in camera traps that do the classification at the edge (MobileNetV3). Models were trained using the TIMM library (Wightman 2019 ) with transfer-learning from ImageNet-22k (Ridnik et al. 2021) , the largest publicly available database. Data augmentation was applied using the imgaug library (A. B. Jung et al. 2020 ) using only standard transformations such as flips, crops, conversion to grayscale and affine transformation.
, 2010; Payrastre et al. , 2011)  or by merging datasets from homogeneous hydrological response regions through a "spatial extension"  (El Adlouni et al. , 2006; Fill and Stedinger, 1998; Halbert et al. , 2016; Jones and Kjeldsen, 2009; Merz and Blöschl, 2003; Wallis et al. , 2007). In recent years, the development of online databases and resources such as the "Chronology of British Hydrological Events" (CBHE, https://www. cbhe. hydrology. org. uk/index. php), the French-German "Observatoire Régional des Risques d'Inondation" (ORRION, https://orrion. fr/,  Giacona et al. , 2019)  or the Swiss Euro-ClimHist (https://www. euroclimhi st. unibe. ch/de/,  Pfister and Rohr, 2015)  datasets, have offered comprehensive historical information and therefore facilitated historical analysis of past floods, even if the development of such databases has been somewhat piecemeal, resulting in different forms of datasets  (Wilhelm et al.
, 2018b). Therefore, in practical applications, unearthing extensive databases on historical extreme floods remains a formidable challenge, posing significant obstacles to conducting thorough flood frequency analysis at local and regional scales. Here, we focus on the reconstruction of historical flood discharges in a torrential alpine catchment since the 14th century and to analyze the extension of a short instrumental timeseries of flood frequency and discharge over several centuries. To this end, we (i) studied a large corpus of historical flood descriptions from this small river and classified historical data into catastrophic, extraordinary, and ordinary events based on the available flood description, (ii) performed 2D-hydraulic modeling to estimate flood magnitudes at different times (and with different riverscape settings), and (iii) applied flood-frequency analyses to define flood return periods.
The inclusion of historical data in flood frequency analysis also yields a more realistic picture of flood quantiles. Fig. 6  provides a dataset of past floods fitted with an LP3 distribution (Table  4 ) and shows quite clearly that discharge at a given probability will be underestimated if analyses are based on systematic measurements alone. In other words, incorporating historical data into the FFA will not only reduce the variance of estimated discharges but also result in a slight increase of flood quantiles (marked with a solid line in Fig. 6 ) compared to values derived exclusively from systematic records (Table  4 ). For example, if all historical floods found in archives are added (i. e. , dashed line in Fig. 6 ), the magnitude of a 100-year return period flood rises from 117 m 3 /s to 134 m 3 /s.
Despite the implementation of a lifting bridge in 1994, flood frequency and magnitude have increased again in 1998-2020. Our results are based on existing and reconstructed datasets. We assume that the multi-century extreme flood distribution documented here can be used to constrain future extreme floods. Including the effect of climate change could have an impact on the flood frequency estimation, but current climate modelling frameworks present significant uncertainties, especially at the local scale. Here, the long-term reconstructed peak discharges exceed the duration of modes of natural climate variability, and thus provide an opportunity to compare past, recent and future floods using different climate scenarios  (Benito et al. , 2023). The gathered information in this study also holds implications for the management of local flood risk.
16S rRNA gene sequence processing Raw demultiplexed paired-end sequences were processed following the Quantitative Insights into Microbial Ecology (QIIME2 ver. 2021. 4) pipeline  (Bolyen et al. , 2019). Briefly, the raw data was first visualized with the demux plug-in function for quality-checking and identifying poor-quality base regions to filter. The sequences were then trimmed, filtered, denoised, chimeras-removed, and merged using the DADA2 plug-in function in QIIME2  (Callahan et al. , 2016). The sequences were identified at a single nucleotide threshold (Amplicon Sequence Variants; ASV;  Callahan et al. , 2017). Additional steps of quality control were performed to reduce non-target sequences by aligning (vsearch alignment method) the ASV sequences to the 99% representative sequences from the greengenes database and only ASV with a similarity threshold of 0.
65% and coverage threshold of 0. 5% were retained following https://github. com/biocore/deblur/issues/139#issuecomment-282143373. Singletons and sequences with less than 349bp were also removed with filter-features and filter-sets function in QIIME2, respectively. The quality-filtered sequences were then taxonomically assigned with a naïve-Bayes classifier trained on the V3-V4 region of the 16S gene in the Silva v138 database  (Quast et al. , 2013) , using the classify-sklearn function in the feature-classifier plugin  (Bolyen et al. , 2019). The output from this generated a taxonomy table with taxonomic naming for each ASV found within our samples. ASV sequences that were identified as mitochondria, chloroplast, archaea, and eukaryotes were filtered out. The quality-filtered ASV sequences were then ready to be used for downstream analysis.
Metabolites that fulfilled both assumptions were tested with one way Analysis of variance (ANOVA) to investigate the variation in metabolites' concentrations across timepoints, followed by post-hoc Tukey's Honest significant difference (Tukey's HSD). For metabolites that violated either one of the assumptions, they were evaluated with GLM (gamma distribution) following a similar procedure as with the body temperature datasets. Assessment of PUFA-oxidised metabolites' beta diversity (with all the 15 metabolites or only the 4 significant metabolites) was performed by first generating a Euclidean distance matrix between samples. A principle Coordinate Analysis (PCoA) plot was then created to visualize the PUFA-oxidised metabolites clustering across different timepoints, using the ggplot2 R package  (Wickham, 2008).
The ANOVA statistical assumptions of normality (Shapiro-Wilk test) and homogeneity of variance (Levene's test) for all datasets were tested and fulfilled (p-value > 0. 05). Linear regression analysis was also conducted to determine the relationship between the oysters' microbiome alpha diversity (i. e. , ASV richness and Shannon-Wiener (H') index) and body temperature (i. e. , average of the 10 random independent body temperature replicates collected at each timepoint). The statistical assumptions for the linear regression model were fulfilled based on the normality test (Shapiro-Wilk test) and residuals plot (variance homogeneity). Beta diversity of the oysters' gut microbiome taxonomic and functional profiles was evaluated by generating the Bray-Curtis dissimilarity distance matrix between samples, and a PCoA plot was then created to visualize microbiome taxonomic and functional clusters across different timepoints, using the ggplot2 R package  (Wickham, 2008).
Acknowledgements. DV is supported by  Cornell Atkinson Center for Sustainability. AR is supported by  NSF  grant  AGS-2017113  and a gift from the  SilverLining Safe Climate Research Initiative. MH is funded by the  Natural Environment Research Council Exeter-NCAR (EX-TEND)   collaborative development grant  ( NE/W003880/1 ) and by  SilverLining  through the  Safe Climate Research Initiative. SW is supported by the  Japan Society for the Promotion of Science  (grant no. JP2103668 ). Support for BK was provided in part by the  National Science Foundation  through agreement  SES-1754740 ,  NOAA 's  Climate Program   Office, Earth's Radiation Budget (ERB)  (Grant  NA22OAR4310479 ), and the  Indiana University Environmental Resilience Institute. The  Pacific Northwest National Laboratory  is operated for the  US Department  of.
The G6sulfur simulations were run out to 2100, for the main reason that that was the end date for most CMIP6 forcing datasets. Decisions around simulation length should account for the crucial question of what the actual point of the experiment is. If the purpose is detecting the time of emergence of the SRM signal (which could be of the order of a decade, globally, or more regionally, depending on the magnitude of the forcing, Keys et al. (2022) ), prioritizing ensemble size over length would be preferable. If it is to understand the long-term Earth system model response to SRM, then one should prioritize longer runs (decades to century timescales). If it is to understand near-term climate change for climate policy decision making (a few decades), a mix of the two priorities may be appropriate.
We first present a method to partition the PAR incident radiation into its direct and diffuse component using a data driven approach. Then, we establish relationships between the diffuse fraction in the PAR domain and each spectral bands using the 6S radiative transfer model  (Vermote et al. , 1997). Then they were used in simulation of hemispherical-directional reflectance factor (HDRF) through a kerneldriven model  (Roujean et al. , 1992)  and PROSAIL model  (Jacquemoud et al. , 2009). Both observations and simulations were converted into relative terms as a substitute of absolute radiometric calibration. Kernel parameters were calculated from the kernel-driven model and diurnal relative multi-band spectrometer measurements. The Artificial Neural Network (ANN) algorithm was employed to retrieve GAI, LCC and CCC from kernel parameters and geometric configurations.
g. culms) was demonstrated to be erectophile (from 70 • to 80 • ), by  Barillot et al. (2019). For this specific range, Fig. 3  shows that G(θ l , θ) can be assumed constant (G-slope = -0. 0013). The semantic segmentation of the gap fraction images relies on a deep learning approach developed on wheat and described and evaluated by  Serouart et al. (2022). This method was composed of two steps. A U-net model was first trained over a large dataset to separate vegetation from background. Then, a support vector machine was used to classify the vegetation masks into green and non-green components. The training dataset of each step is independent using wheat at different phenological stages in a variety of environments and under different illuminations. The gap fraction of each RGB image was calculated as the ratio of the number of non-green pixels to the total number of pixels in the image.
The SPAD measurement dates were different among fields, but generally every 10 to 20 days on key growth stages. These SPAD raw readings should be converted into content of chlorophyll (μg/cm 2 ) using relationships with absolute LCC measured in laboratory. Since there was no absolute LCC measurement in this study, we proposed an ensemble method to compute the reference. First, we applied multiple equations in literatures (Table  S2 ) to convert our SPAD readings to LCC. Then the median of all LCC from single SPAD reading was computed as reference to validate corresponding IoTA retrieval. The standard deviation of LCC from different equations was used as the uncertainty of the reference dataset. Only equations built with only wheat dataset or including wheat were selected. The total canopy chlorophyll content (g/m 2 ) was then obtained by multiplying the GAI and the LCC that were measured within three days.
2. 2 to generate the training database. The inputs included instantaneous total PAR, d(t) and optical path defined as the cosine of sun zenith angle, and the output was the instantaneous diffuse PAR. The database has in total 84,587 cases, and it was divided into two parts: 69659 samples were randomly selected to train the neural network, and the remaining 14,928 samples were used for overfitting control and evaluation of the theoretical performances. We found that a Back Propagation Artificial Neural Network (BP-ANN) made of one hidden layer of four neurons characterized by a tangent-sigmoid transfer function followed by a single linear transfer function output neuron provided the best results, e. g. , no overfitting and best accuracy. Ten BP-ANN-DF were trained and the performance using the training dataset was shown in Fig.
Generation of PROSAIL training database Similarly to  Li et al. (2015)  and  Weiss et al. (2020a) , a training database was simulated thanks to the PROSAIL radiative transfer model that couples PROSPECT  (Jacquemoud and Baret, 1990)  to simulate the leaf optical properties and SAIL  (Verhoef, 1984 (Verhoef, , 1985) )  to generate top of canopy reflectance. Table  1  lists the range and distribution law for all variables of PROSAIL model. The soil reflectance data was simulated using five typical soil reflectance spectra multiplied by a brightness coefficient allowing to increase the diversity in actual soil properties  (Weiss et al. , 2020b). This resulted in a total of 41,472 combinations of canopy structures, leaf biophysical properties and backgrounds. For W. Li et al. every combination, the hemispherical-directional reflectance ρ HDR,λ (θ s , θ v , ϕ)and bi-directional reflectance ρ BRDF,λ (θ s , θ v , ϕ) at waveband λ  (Schaepman-Strub et al.
Training and applying the BP-ANN-V Following  Weiss and Baret (1999)  and  Li et al. (2015) , we trained one BP-ANN-V per each variable. The training database was divided into two parts: two thirds of the simulations were randomly selected to train the neural network and the remaining one third was used for validation. The inputs were the 18 kernel parameters, and the output was either GAI, LCC or CCC. Each neural network was made of one input layer, one hidden layer composed of 5 neurons with tangent sigmoid transfer.
PAR diffuse fraction The results presented in Fig. 5  show that, over our experimental sites, the PAR diffuse fraction can be accurately estimated from the total PAR, sun zenith angle and a correction coefficient accounting for variations in sun-Earth distance, using the BP-ANN-DF neural network (R 2 = 0. 86, RMSE = 0. 11). Our dataset mainly includes low diffuse fraction values (lower than 0. 5), mostly corresponding to the Avignon site in Southern France characterized by frequent sunny illumination conditions and small aerosol concentration values, and very high diffuse fraction values (>0. 9), mostly corresponding to the Ouzouer site in Northern France which presents frequent overcast illumination conditions. Additional medium PAR diffuse fraction measurements might thus be required to ensure a similar accuracy over the intermediate range of values.
Validation against ground measurements The retrieved biophysical variables were compared with the corresponding ground measurements at the same dates. The GAI estimated from IoTA systems has a very good consistency with the field data taken by RGB cameras (R 2 = 0. 86, RMSE = 0. 54, relative RMSE = 26. 95%, Fig. 8a ). The LCC retrieval from IoTA has very similar correspondences with reference LCC converted using various equations (R 2 = 0. 51-0. 53) but quite different scattering and biases (RMSE = 5. 59 μg/cm 2 -18. 94 μg/ cm 2 , relative RMSE = 12. 08% -65. 51%) (Fig. S8 ). The best correlation and minimum RMSE was obtained using calibration equation No. 4 and 12, while the largest RMSE was found using equation No. 6. Using   c ) Spectral diffuse fractions estimated from polynomial relationships versus the ones measured by the AIRPHEN camera using PAR diffuse fraction of 0.
9 as the threshold value to discriminate the cloudy conditions. R 2 , RMSE and RRMSE were computed over all bands. RRMSE is relative RMSE calculated as the ratio between RMSE and average value. proposed ensemble method which took the median of all equations as the reference, IoTA LCC exhibits a good correlation but a systematic overestimation (R 2 = 0. 52, RMSE = 12. 06 μg/cm 2 , relative RMSE = 33. 34%) (Fig. 8b ). The uncertainty of reference LCC ranges from 4. 21 μg/cm 2 to 11. 78 μg/cm 2 , and the overall uncertainty is 7. 44 μg/cm 2. Conversely to LCC, the chlorophyll content at canopy level presents a good correspondence between ground reference datasets and estimations from IoTA systems (R 2 = 0. 93, RMSE = 0. 25 g/m 2 , relative RMSE = 26. 12%, Fig. 8c ). Since the RGB images and SPAD measurements were taken on different dates and sites, only few measurements can be used to calculate the ground CCC values.
(2018)  applied Look-up tables in the inversion, while  Deng et al. (2006)  and  Roujean and Lacaze (2002)  normalized multi-angle reflectance into specific view angles using kernel-driven models and estimated biophysical variable using normalized values. However, simple application of above methods is not feasible in the continuous spectrometer measurements. In this study, some invalid measurements (e. g. , sun zenith angle larger than 60 • or negative measurements) were removed before the inversion and the sun position vary every day, resulting in different number of valid measurements and corresponding geometry per day. This will increase the dimension of training database for look-up table and impact the efficiency in the inversion. Alternatively, we combined the kernel-driven model and PROSAIL model and estimated biophysical variables from an BP-ANN algorithm.
, 1996). More efforts are required in the future to deeply study the influences of radiometric calibration on LCC estimations using data at canopy level. The early version PROSPECT model  (Jacquemoud and Baret, 1990)  was used in this study, while several improved PROSPECT models were proposed, such as PROSPECT-D  (Féret et al. , 2017)  and FASPECT  (Jiang et al. , 2021)  which have been proved to have better LCC retrieval accuracy  (Berger et al. , 2018; Jiang et al. , 2021; Li et al. , 2020). Further, the range of input parameters for generating the training database also has direct impacts on the retrieval accuracy. For example, chlorophyll a + b content for wheat varies within 0-90 μg/cm 2 , e. g. , 10-80 μg/cm 2 in  Li et al. (2022) , 0-80 μg/cm 2 in  Berger et al. (2018)  and 20-90 μg/cm 2 in  Delloye et al.
(2018). We finally set the range to 20-80 μg/cm 2 based on prior knowledge of the wheat in our study area. The ALA of wheat was demonstrated to be erectophile (from 70 • to 80 • ), therefore, the ALA for PROSAIL model was set to 60 • -80 • with mean of 70 •. This is consistent with  Dong et al. (2019)  which characterized wheat by erectophile leaf angle distribution with mean ALA of 62. 49 • and standard deviation of 11. 08 •. The accuracy of estimated GAI, LCC and CCC relies on the ground reference dataset. In this study, reference GAI was estimated from gap fraction based on segmentation of green pixels on RGB images. We used a robust deep learning method trained on a comprehensive dataset composed of a variety of species, instruments, and environmental conditions  (Madec et al. , 2023; Serouart et al.
, 2022)  to guarantee the accuracy of segmentation. It is worth noting the segmentation might be difficult when GAI is larger than 4 or 5. The derivation of the GAI reference dataset assumes a constant G-function for a 45 • viewing direction. This is actually very similar to the approach validated by  Campos-Taberner et al. (2016)  and  Francone et al. (2014) , who used the 57. 5 • angle for which the G-function is constant regardless of the leaf inclination angle value. We preferred using a 45 • which show less saturation for dense canopies and eases the RGB image segmentation. At 45 • , G can still be assumed constant for wheat canopies that were shown erectophile (mean inclination angle between 70 • and 74 • as estimated by  Barillot et al. (2019)  for different wheat types). Additionally, this allows to be consistent with the inclination angle of the multi-band spectrometer.
Acknowledgements This work was supported by  French FUI IOTA  project ( FUI 24 -D0S0071619 ). The authors acknowledge  Guy Deshayes  and  Simon Madec  ( Arvalis ) for their kind supports with reference data collection and processing and  Jeremy Labrosse  ( HIPHEN ) for his valuable supports on IoTA systems' maintenance. Wenjuan Li  acknowledges support from  Central Public-interest Scientific Institution Basal Research Fund  (No. CAAS-ZDRW202107 ) and  National Natural Science Foundation of China  ( 42201388 ). The authors are thankful to the anonymous reviewers for their constructive criticism which contributed to improve substantially the paper.