dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors would like to acknowledge  Nolwenn Verpy ,  Laure Papillon  and  Deny Malengros  for help collecting samples,  Caroline Gauthier  for help with OC analysis,  Marc Souhaut , and  Thomas Zambardi  for help with 7 -Berylium analysis at the LAFARA underground laboratory. We would like to thank  Hugo Lepage  for discussing river flows and helping us obtain the SPM data and also the  Observatoire des Sédiments du Rhône  staff for the SPM results. Finally, we would also like to thank the various members of the  RV Antédon II  crew who were present on several campaigns. The map 525 in this study was created with the ODV Software Schlitzer, Reiner, Ocean Data View, https://odv. awi. de, 2022. Financial support. This work was supported by a grant from the special call on estuaries from  Frenc h National programme EC2CO  under the name " DeltaRhone " and the  French  government grant managed by the  Agence Nationale de la Recherche  under the  France 2030 program ,  RiOMar  reference  ANR 22 POCE 0006.
In this study, new data-driven parametrizations have been developed for surface turbulent fluxes of momentum, sensible heat and latent heat in the Arctic. Machine learning has already been used outside the polar regions to provide accurate and computationally inexpensive estimates of surface turbulent fluxes. To investigate the feasibility of this approach in the Arctic, we have fitted neural network models to a reference dataset (SHEBA). Predictive performance has been tested using data from other observational campaigns. For momentum and sensible heat, performance of the neural networks is found to be comparable to, and in some cases substantially better than, that of state-of-the-art bulk formulations. These results offer an efficient alternative to the traditional bulk approach in cases where the latter fails, and can serve to inform further physically based developments.
A popular approach to measuring out-of-sample performance is K-fold cross validation (CV), which entails splitting the data into K independent subsets. For each k ∈ {1,. , K}, the model is Ątted to those data not contained in subset k. The Ątted model is then used to predict the response variable in subset k. Iterating over the K subsets, one thus obtains a complete dataset of out-of-sample predictions from which to compute performance metrics such as root-mean-square error (RMSE) or mean absolute error (MAE). Statistical independence of the K subsets is a necessary condition for cross-validated performance metrics to be meaningful. In a typical machine-learning problem, genuinely independent subsets are not available and are instead manufactured by randomly partitioning a single dataset into K folds.
Datasets of surface turbulent Ćuxes are typically measured as time series, in our case none longer than a year, which invalidates the independence assumption of the random subsetting approach. While one alternative would be to construct an elaborate stratiĄed cross-validation scheme, intended to account for temporal (and other sources of) autocorrelation, we instead opt to make use of the fact that in this study we have a database of observations from multiple Ąeld campaigns. The observations were collected at a range of locations, at different times, using different measurement instruments, by different teams of researchers and under different meteorological conditions. For example, data were collected at towers, from ship masts and from low-Ćying aircraft. In theory, cross validation of a Ćux parametrization across observational campaigns estimates how well we would expect it to perform at predicting data collected in a future campaign, which would seem an appealingly objective metric for comparison.
Results Performance metrics computed from the cross-validation experiments are given in Table  1. Measured differences in performance between the bulk and neural-network methods were tested for statistical signiĄcance on a per-dataset basis using a bootstrapping approach  (Davison and Hinkley, 1997). Model-predicted turbulent Ćuxes in kinematic units are plotted against Ąeld observations from the ACSE dataset in Figure  2. Results for ACSE are fairly representative of the results across the other datasets, with the notable exception of the ACCACIA Ćight data. Equivalent Ągures for the other datasets, including ACCACIA, are provided in the supplementary information. It can be seen from these results that, of the three turbulent Ćuxes {u 2 ⋆ , u ⋆ θ ⋆ , u ⋆ q ⋆ }, the momentum Ćux u 2 ⋆ is by far the easiest to predict due to its strong linear correlation with squared windspeed u 2.
Compared with the bulk algorithm, the neural-network model performs similarly well or better on all datasets, except for the SHEBA tower. Performance at the SHEBA tower is expected to be worse because that dataset accounts for most of the available observations and removing it reduces the training set to a small fraction of its former size. The greatest proportional error reduction was seen in the AO16 dataset where the neural-network model gave a 24 % reduction in RMSE. In general, bulk estimates of u 2 ⋆ are very good, so there is little room for large performance improvements, especially considering the fact that we are dealing with noisy real-world observations. There is a visible negative bias in the neural network estimates for large values of u 2 ⋆ in the ACSE dataset (see Figure  2 ), which is an artifact of the conservative extrapolatory properties of the neural network model (see next paragraph).
It should be noted that the bulk algorithm exhibits a similar negative bias in other datasets, e. g. AO16. Due to the lack of boundary constraints on its output, the neural-network model occasionally produces very small negative estimates of u 2 ⋆. In this study such estimates have been rounded up to zero before computing performance metrics, as this step can easily be included when the parametrization is implemented in a GCM. Estimation of u 2 ⋆ in a log-transformed space, a common method for enforcing strict positivity, is inappropriate here, since in a GCM it is the magnitude of the Ćux in an absolute rather than relative sense which is important. The neural-network parametrization of the sensible heat Ćux u ⋆ θ ⋆ was universally successful, performing similarly to or substantially better than the bulk algorithm on all of the datasets.
For the ACCACIA Ćight data, the reduction in RMSE was 57 %, which seems remarkable considering that for those data the measurement height z is well outside the range observed in the other datasets. In this case, extrapolating beyond the assumptions of the MOST (the Ćights took place at a height for which the assumption of constant Ćuxes down to the surface might very well not hold) appears to be more harmful than extrapolating the neural network beyond the conditions seen in the training set. Despite exhibiting greater RMSE, the bulk-algorithm estimates of u ⋆ θ ⋆ are more strongly correlated with the ACCACIA observations because of the large inĆuence of the most extreme Ćuxes. There is a simple explanation for this: since some of the Ćuxes observed in the ACCACIA Ćights were much larger than anything seen at the SHEBA tower, which constitutes the majority of the training set, the neural-network model tended to grossly underestimate the magnitude of these Ćuxes.
Decreasing the number of hidden nodes does reduce the estimator variance, but at the same time worsens the bias such that the overall performance remains poor. A more likely explanation is that the training dataset is simply insufficient in its information content to train a generalizable machine-learning model. Latent-heat-Ćux observations are comparatively scarce, and of those available in this study the SHEBA tower contributes 94 %. Unfortunately, the range of Ćux magnitudes observed at the SHEBA tower was small in comparison to the other datasets. Given the superior performance of the neuralnetwork parametrizations of u 2 ⋆ and u ⋆ θ ⋆ , and given that the bulk algorithm estimates u ⋆ q ⋆ with reasonable accuracy at the SHEBA tower (69 % correlation), it stands to reason that a training set which sampled densely a wider range of Ćux values might enable a similarly performant machine-learning parametrization.
In this study, it has been proposed to encode in a machine-learning model the relationships observed in practice between surface Ćuxes and meteorological predictor variables in the Arctic. To investigate feasibility of the data-driven approach, we have trained neural-network models using a database of observations assembled from several Arctic Ąeld campaigns. A bulk-algorithm implementation, containing advanced polar-speciĄc parametrizations from the literature, has been used as a benchmark against which to test performance of machine-learning models. Using a cross-validation scheme, the out-of-sample predictive accuracies of the bulk and neural-network parametrizations have been objectively compared. The neural-network parametrizations of the momentum and sensible heat Ćuxes were found to match and in some cases outperform their bulk counterparts in a RMSE sense.
Acknowledgements This is a contribution to the Year of Polar Prediction (YOPP), a Ćagship activity of the  Polar Prediction Project  (PPP), initiated by the  World Weather Research Programme (WWRP)  of the  World Meteorological Organisation (WMO). We acknowledge the  WMO WWRP  for its role in coordinating this international research activity. The neural-network architecture schematic in Figure 1 was created using the plotnet function in the NeuralNetTools package for R (Beck, 2018). Fitting of neural network ensembles was automated with the help of wrapper functions in the R package caret.
Funding This work was supported by a national funding by the  Agence Nationale de la Recherche  within the framework of the  Investissement dŠAvenir programme  under the  ANR-17-MPGA-0003  ASET reference. This article has received funding from the  European UnionŠs Horizon 2020 research and innovation programme  under grant agreement No  101003826  via project  CRiceS (Climate Relevant interactions and feedbacks: the key role of sea ice and Snow in the polar and global climate system).
Data availability ACCACIA Ćight data are available from the CEDA archive: • https://doi. org/10. 5285/0844186db1ba9e20319a2560f8d61651 (MASIN); • https://catalogue. ceda. ac. uk/uuid/c064b0c150274a1cbd18c563573f392e (FAAM). ACSE cruise data are available from the CEDA archive (https://doi. org/10. 5285/ c6f1b1ff16f8407386e2d643bc5b916a, Brooks et al. , 2022a). AO16 cruise data are available from the CEDA archive (https://doi. org/10. 5285/ 614752d35dc147a598d5421443fb50e8,  Brooks et al. , 2022b). SHEBA data are available from the UCAR Earth Observing Laboratory (https:// data. eol. ucar. edu/dataset/dsproj?SHEBA).
The ACCACIA Ąeld campaign was supported by the UK Natural Environment Research Council (NERC) grant numbers NE/I028653/1, NE/I028858/1, and NE/I028297/1. The participation of Ian Brooks and John Prytherch in the ACSE Ąeld campaign was supported by NERC grant number NE/K011820/1. The AO16 measurements were supported by the the Swedish Polar Research Secretariat. John Prytherch was also supported by the Knut and Alice Wallenberg Foundation (grant number 2016-0024).
Recent updates of pan-European seismic hazard and risk maps adopted the partially non-ergodic Kotha et al. (  2020 ) ground-motion model. This model was regressed from the Engineering Strong Motion dataset, containing ground-motion data of 𝑀 𝑊 ≥ 3 events mostly from Italy, Turkey, Greece, and in smaller fractions from rest of the active shallow crustal tectonic regions of Europe. Through mixed-effects regressions, the non-ergodic model partially resolved the spatial variability of attenuation characteristics across most of seismically active Europe, but not in France due to the then lack of a regional dataset. With the availability of a manually processed dataset from Résif network, and a computationally viable Bayesian inferencing algorithm, this study extends the non-ergodic applicability of the model to 𝑀 𝑊 < 3 earthquakes, attenuating regions, tectonic localities, and sites located in France.
In process, a few important decisions had to be made concerning the updating methodology, and the interpretation of spatial variability of attenuationspecifically, that of the tectonic localities producing earthquakes. The methodology and results are discussed, emphasising the need to revise the current ground-motion regionalisation approach, and to tailor the updating procedure to be application specific. This study anticipates and supports a shift from frequentist to Bayesian approach of ground-motion modelling, in order to maintain continuity of knowledge regressed from various ground-motion datasets.
The ESHM20 GMM logic-tree for active shallow crustal earthquakes  (Weatherill, Kotha and Cotton 2020 ) is an application driven implementation of the  Kotha et al. (2020)  and  Kotha et al. (2022)  GMMs. Together, these GMMs together will be referred to as K20 from hereon. The K20 models were regressed from the Engineering Strong Motion (ESM) dataset developed and validated by  Lanzano et al. (2018)  and  Bindi et al. (2018). The ESM dataset featured ground-motions recorded in several seismically active Euro-Mediterranean regions hypothesised to exhibit strong spatial variability of attenuation characteristics. To quantify the spatial (or regional) variability of ground-motion attenuation characteristics, K20 relied on geology and tectonics based regionalisation models of  Basili et al. (2019)  and  Danciu et al.
In level 1 of the logic-tree shown in Fig. 1 , 𝛿𝑐 3,𝑟 is the adjustment to the 'apparent anelastic attenuation' coefficient (𝑐 3 in K20) specific to the region 𝑟 hosting the site, and 𝑆𝐸(𝛿𝑐 3,𝑟 ) is the uncertainty on 𝛿𝑐 3,𝑟. In level 2, 𝛿𝐿2𝐿 𝑙 is the adjustment to the 'offset/bias/intercept' coefficient (𝑒 1 in K20) specific to the tectonic locality 𝑙 hosting the event, and 𝑆𝐸(𝛿𝐿2𝐿 𝑙 ) is the uncertainty on 𝛿𝐿2𝐿 𝑙. To ensure that the resulting ground-motions follow a Gaussian distribution, one may set 𝜂 = 1. 732 with branch weights 𝑊 1,𝑗 = 0. 167, 0. 666, 0. 167 for 𝑗 = 1, 2, 3  (Miller III and Rice 1983 )similar to those in ESHM20 GMM logic-tree. The logic-tree is cast as such so that [ 𝛿𝑐 3,𝑟 , 𝑆𝐸(𝛿𝑐 3,𝑟 )], [ 𝛿𝐿2𝐿 𝑙 , 𝑆𝐸(𝛿𝐿2𝐿 𝑙 )], 𝜂, and the branch weights can be modified and adapted to new regions when and where new ground-motion datasets beyond ESM become available.
This study is a step towards updating the K20 GMM using the Résif ground motion dataset by  Traversa et al. (2020)  recently extended to the end of 2021 by  Buscetti et al. , (in-prep. )  and adapting the Fig. 1  backbone logic-tree to France. A modest updating procedure would be to compare the ground-motion distributions from the GMM logic-tree against the new data from a region, and iterativelyand exclusivelymodify the 𝛿𝑐 3,𝑟 , 𝛿𝐿2𝐿 𝑙 , or other GMM coefficients. There are at least three problems that deter such a simplified approach: 1) GMM fixed-effect coefficients are often correlatedthis challenges exclusively adjusting any coefficient while leaving the rest unchanged; 2) the new dataset may have sampled [𝑀 𝑊 , 𝑅 𝐽𝐵 ] ranges beyond the GMM's applicabilitythis may require evaluating first and then recalibrating the GMM to extend its usability; 3) the new dataset may have sampled [𝑀 𝑊 , 𝑅 𝐽𝐵 ] scenarios well within GMM's applicability, but the event-, path-, and site-effects, and their combinations may be rather peculiarthis may lead to misattributing, for example, systematically strong site-effects as event-and path-effects.
In combination, these three issues may render iterative estimation of physically meaningful 𝛿𝑐 3,𝑟 and 𝛿𝐿2𝐿 𝑙 rather challenging, and possibly unreliable. Incidentally, this was the case with the Résif dataset of French ground-motions. It was impossible to guess via residual analyses, for example, if the French earthquakes were systematically stronger, if the French site amplifications were stronger, or if the French regional crust attenuated ground-motions rather weakly compared to the pan-European average of K20. Therefore, this study required shifting from classical or frequentist mixed-effects regressions to Bayesian mixed-effects GMM regressions  (Samaniego 2010). Essentially, this study first recasts the K20 GMM in a Bayesian framework. Following an implementation and evaluation of the new regression approach, the K20 model is updated using the French dataset.
DATASETS The pan-European dataset (ESM, yellow markers and histograms in Fig. 2 ) was used in conjunction with the regionalisation model of  Basili et al. (2019)  and tectonic localisation model of  Danciu et al. (2021)  in deriving the K20 GMMs. Details on each of these are available in their respective publications, and will be skipped here. The data selection procedure for the robust linear mixed-effects regression (RLMM; robustlmm by Koller 2016) of K20 is also described in  Kotha et al. (2020). The companion dataset from France by  Traversa et al. (2020)  contains ground-motion data recorded by Résif network (Résif, blue markers and histograms in Fig. 2 ). The key features of the datasets relevant to this study are: 1) Prior to data selection procedure based on usable frequency range with good signal-to-noise ratio, while ESM contains data from 3 < 𝑀 𝑊 < 8 events, Résif dataset contains data from a largely disjoint range 2 < 𝑀 𝑊 < 5.
This means that, the new data is well beyond the applicability range of K20, particularly towards lower 𝑀 𝑊. 2) The preferred 𝑀 𝑊 of events in ESM dataset are those from the EMEC catalogue  (Grünthal and  Wahlström 2012) revised by  Weatherill. G. and Lammers. S. (GeoForschungsZentrum, GFZ Potsdam)  during ESHM20 development. The 𝑀 𝑊 estimates of Résif events are derived predominantly from the SIHEX-BCSF-RENASS catalogue  (Cara et al. 2015)  and from conversions from local-magnitude estimates. In this study, the 𝑀 𝑊 provided in the Résif dataset are preferred even if EMEC values are available for some of the events.
METHOD The published K20 GMM was derived using a robust linear mixed-effects regression (RLMM) algorithm of the robustlmm package in R (R-Core-Team 2000, RStudio-Team 2022). The robust regressions were necessary to identify and down-weight possible outlier ESM data from biasing the fixed-effects median and random-effect variances of the mixed-effects K20 GMM. Although RLMM regressions could provide the maximum-likelihood estimates of GMM fixed-effects coefficients and random-effects variances (and standard-deviations), they do not inform on uncertainties of these quantities. Uncertainties are joint distributions of GMM mixed-effects estimates that could allow a modeller understand which components are relatively better constrained, and which ranges of the dataset may require better sampling.
In addition, Bayesian approach to ground-motion modelling overcomes most of the above issues. A Bayesian regression yields joint distributions of the mixed-effects parameters of a GMM. These joint distributions help in assessing the strengths and weaknesses of the GMM; but more importantly, instead of performing a new regression on a new and extended dataset, these joint distributions of mixed-effects can be used as informative priors in developing a new GMM or updating an existing GMM. Several authors have argued for the need and advantages of Bayesian approach in ground-motion modelling (e. g. Kowsari et al. 2019 , Stafford 2019 , Kuehn and Scherbaum 2016 , Arroyo and Ordaz 2010 , Wang and Takada 2009). Moreover, with the recent developments in approximate Bayesian inferencing using Integrated Nested Laplace Approximation (INLA; Rue, Martino and Chopin 2009), Bayesian GMM regressions have become computationally much more viable (e.
Bayesian inference of K20 GMM from ESM dataset The functional form of K20 GMM is shown in equations  (1 -4). The purpose of its fixed-effects (𝑒 1 , 𝑏 1 , 𝑏 2 , 𝑏 3 , 𝑐 1 , 𝑐 2 , 𝑐 3 ), random-effects (Δc 3,r , ΔL2L l , ΔB e,l 0 , ΔS2S s ), and residuals (Ε) are explained in  Kotha et al. (2020) ,  Kotha et al. (2022) ,  Kotha, Bindi and Cotton (2022) , and will be skipped here. ln(μ) = e 1 + f R,g (𝑀 𝑊 , 𝑅 𝐽𝐵 ) + f R,a (𝑅 𝐽𝐵 ) + f M (𝑀 𝑊 ) + ΔL2L l + ΔB e,l 0 + ΔS2S s + Ε  f R,g = (c 1 + c 2. (𝑀 𝑊 -M ref )). ln √(𝑅 𝐽𝐵 2 + h D 2 )/(R ref 2 + h D 2 ) (2) f R,a = c 3 + Δc 3,r 100. (√𝑅 𝐽𝐵 2 + h D 2 -√R ref 2 + h D 2 ) (3) f M = { b 1. (𝑀 𝑊 -M h ) +b 2. (𝑀 𝑊 -M h ) 2 𝑀 𝑊 ≤ M h b 3. (𝑀 𝑊 -M h ) M h < 𝑀 𝑊 (4) The exact same subset of ESM dataset used in deriving the K20 model is used in this study.
Bayesian update of K20 GMM with Résif dataset The data selection procedure described in the Datasets section resulted in 15586 records in Résif dataset available for the Bayesian update at 𝑇 = 0. 01𝑠; which falls to 10450 at 𝑇 = 1𝑠, and 3558 at 𝑇 = 2𝑠. The marginal distributions of K20 mixed-effectsthe yellow curves in Fig. 5  and Fig. 6  can be used as informative priors in the Bayesian update using Résif dataset. Initial attempts with the inla function allowed simply updating the K20 mixed-effects with the natural-log of ground-motion values in Résif dataset as likelihoods. Following the INLA package update (version 23. 04. 24) the regressions have become unstable and produced nonsensical GMM coefficients. To remedy this, the Bayesian update is performed on the 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝑠 obtained by subtracting K20 fixed-effects (median, equations 2-4) prediction from natural-log of Résif ground-motions (ln (𝐺𝑀)), as in equation (  5 ).
Only the trial considered to be producing the most defensible GMM update is discussed here. These restrains were placed on specific fixedeffects; meaning, certain coefficients among (𝑒 1 , 𝑏 1 , 𝑏 2 , 𝑏 3 , 𝑐 1 , 𝑐 2 , 𝑐 3 ) were restrained from being updated: • 𝑒 1 is the offset, bias or intercept of the GMM median (equation 1). This fixed-effects coefficient is indispensable in a GMM regression, but has no strict physical meaning. A positive 𝛿𝑒 1 (equation 6) following the update would shift the GMM median to higher values for all [𝑀 𝑊 , 𝑅 𝐽𝐵 ] combinationsboth ergodic and non-ergodic predictions. This can be considered indefensible because the Résif dataset does not have the same [𝑀 𝑊 , 𝑅 𝐽𝐵 ] range as ESM, and the updated coefficients should (preferably) not effect predictions beyond the [𝑀 𝑊 , 𝑅 𝐽𝐵 ] range of the new dataset.
In addition, the 𝛿𝐿2𝐿 𝑙 values are added to 𝑒 1 to obtain partially non-ergodic predictions via the GMM logic-tree (level 2 in Fig. 1 ). Since, the K20 estimate of 𝑒 1 is a pan-European average, modifying 𝑒 1 may render the ESM estimates of 𝛿𝐿2𝐿 𝑙 incompatible with K20. In order to be able to compare 𝛿𝐿2𝐿 𝑙 of the new Résif tectonic localities (in left panel of Fig. 4 ) to those estimated using ESM, it was deemed necessary to restrain 𝑒 1 from updating. Therefore, 𝑒 1 is not updated by fixing 𝛿𝑒 1 = 0 𝑖𝑛 equation (  6 ). • 𝑏 1 , 𝑏 2 fixed-effects coefficients control the scaling of ground-motions with 𝑀 𝑊 (equation 4) for events with 𝑀 𝑊 ≤ 𝑀 ℎ = 5. 7. Since Résif dataset contains data from events with 2 < 𝑀 𝑊 < 5, it is necessary to allow 𝑏 1 , 𝑏 2 to be updated. It is important to note that, 𝑒 1 is positively correlated to 𝑏 1 , 𝑏 2 (discussed in  Kotha et al.
2022). Therefore, constraining 𝛿𝑒 1 = 0 or not strongly effects the update of 𝑏 1 , 𝑏 2. In this study, 𝛿𝑏 1 , 𝛿𝑏 2 in equation (  6 ) are allowed to take non-zero values. • 𝑏 3 fixed-effects coefficient controls the scaling of ground-motions with 𝑀 𝑊 (equation 5) for events with 𝑀 𝑊 > 𝑀 ℎ = 5. 7. Although 𝑏 3 is allowed to be updated, the [𝑀 𝑊 , 𝑅 𝐽𝐵 ] range of the Résif dataset should not affect it. In this study, 𝛿𝑏 3 in equation (  6 ) is allowed to obtain non-zero values. • 𝑐 1 , 𝑐 2 fixed-effects coefficients control the linear-decay of ground-motions with 𝑅 𝐽𝐵 and [𝑀 𝑊 , 𝑅 𝐽𝐵 ], respectively. Equation (2) models the geometric attenuation of ground-motions via weakly correlated 𝑐 1 , 𝑐 2 (discussed in  Kotha et al. 2022). Bindi and Kotha (2020)  observed that geometric spreading could be region-specific due to regional differences in near-surface crustal structure, and seismogenic depths.