dataesr/scientific-paragraphs-categorization huggingface dataset:
| School European meta-analysis data indicate that food allergy affects 4%-7% of primary school children, and approximately 8%-20% of paediatric food-related accidental reactions and anaphylaxis reactions may occur at school. 114 Peanut was the most common trigger among 105 cases of food-induced anaphylaxis (25% of cases) at school documented in a French national allergy database  115  and was the second most common food trigger of anaphylaxis (16%), after tree nuts (23%), occurring in school settings in one German study (n = 87 anaphylaxis cases). 115, 116 A US study of all food-allergic reactions due to accidental exposure at school found that 25%-29% of reactions were attributed to peanut. 117 In a survey study of parents of children with PA or tree nut allergy in the US Peanut and Tree Nut Registry (n = 4586), 16% of respondents reported a reaction at daycare, preschool or elementary school.
Study n Age cohort (mean age, y) Study period Region Criteria for PA (study type) PA prevalence estimate(s), % This study was an update of the HealthNuts cohort reported by Osborne et al, 2011 (line 5 of this table) to assess rate of peanut allergy at age 4 in subjects reported to have peanut allergy at age 1 Gupta et al (2018) 40 38,408 Children (NR) 2015-US Self-report + convincing history (online survey) 2. 2 overall 2016 Lieberman et al (2018) 150 NR Children aged 2017 US Longitudinal analysis of PA diagnosis codes/services 2. 2 overall 4-17 (healthcare claims database) Gonzalez-González 365 Children aged 2015-Honduras OFC of children showing sensitization 0. 8 overall et al (2018) 151 1-18 2016 Ziyab (2019) 34 3864 Children aged 2016-Kuwait Self-report + convincing history (child-and parent-1.
ACKNOWLEDGMENTS First, I thank  M. Genoni  for several useful suggestions for this Tutorial. More generally speaking, the content of this text was influenced by stimulating discussions throughout the years with many colleagues, notably  F. Grosshans ,  R. Filip ,  Q. He ,  M. Gessner , and  G. Ferrini. Furthermore, I am very grateful to  M. Fannes  for teaching me the mathematical foundations that lie at the basis bosonic quantum systems. This point of view was complemented by colleagues in the multimode.
, 2018). Traditionally, apparent (i. e. estimated representation of) OM reactivity has been determined through laboratory-based incubation experiments in shallow (few centimetres deep) and mixed sediments (e. g. Dai et al. , 2009; Grossi et al. , 2003; Westrich and Berner, 1984)  or integrated model-data approaches in deeply buried sediments (hundreds of centimetres to metres deep and hundreds to thousands and millions of years old) (e. g. Boudreau and Ruddick, 1991; Middelburg, 1989; Wehrmann et al. , 2013) , using reaction-transport models (RTM). The latter are powerful tools since they allow the extraction of quantitative measures of OM reactivity from comprehensive multi-species porewater datasets integrated over greater sediment depths and ages (e. g. Middelburg, 2019). However, due to our limited quantitative understanding of the environmental controls on OM reactivity, the application of RTM approaches to data-poor settings is still complicated by the difficulty in constraining model parameters.
The need for predictive frameworks that constrain OM degradation model parameters has been recognized since the early days of RTM. Based on the rationale that the reactivity of OM settling onto the sediment is controlled by the degree of degradation, and thus its residence time in the oxygenated water column, early efforts have explored correlations between OM reactivity and water depth, sedimentation rate, or OM fluxes  (Boudreau, 1997; Boudreau and Ruddick, 1991; Müller and Suess, 1979; Tromp et al. , 1995). However, the derived relationships were based on limited datasets, and a more recent compilation of global data did not reveal statistically significant correlations between OM reactivity and single depositional environmental descriptors  (Arndt et al. , 2013). However, the latter combined OM degradation model parameters derived from structurally different models and used observational data of different complexity, thus somewhat compromising the comparability of their findings.
Observational data We benefit from previously published datasets to develop our large-scale OM reactivity assessments. TOC and porewater SO 2- 4 depth profiles as well as site-specific physical characteristics and supporting data are available for all sites and were compiled from the PANGAEA database (https: //www. pangaea. de, last access: 20 November 2020) (except from Rhone delta and Severn estuary datasets), as well as corresponding published literature (see below). Specific details about sampling strategies and analytical methods can be found in the respective publications. The dataset for the Severn estuary (Table  2 ; row A) is from  Thomas (2014). Rhone delta data (Table  2 ; rows B and C) are from the DI-CASE project  (Rassmann et al. , 2016). Data for the northern European margin sites Aarhus Bay, Arkona Basin, and the Skagerrak transect (Table  2 ; rows D, E, G, H, and I) are part of the Europe-funded project METROL  (Aquilina et al.
, 2010; Dale et al. , 2008a, b; Knab et al. , 2008; Mogollón et   , 2012). Data from the Helgoland mud area, North Sea (Table  2 ; row F)  (Henkel and Kulkarni, 2015) , the Arabian Sea oxygen minimum zone (OMZ) transect (Table  2 ; rows J, K, and L)  (Bohrmann and cruise participants, 2010a, b, c) , Bering Sea (Table  2 ; row M)  (Gersonde, 2009) , and Argentine Basin (Table  2 ; row N)  (Henkel et al. , 2011)  are presented in the PANGAEA database (see above). We use those site-specific datasets to inform our data-model analysis and to constrain OM reactivity and related benthic processes.
Furthermore, downcore changes in concentrationdepth profiles may also be influenced by transient features in bottom water conditions and/or fluxes, and the validity of the steady-state assumption thus needs to be carefully evaluated. Finally, multiple parameter sets might fit the observations equally well. Because both a and v exert an influence on the apparent OM reactivity and its evolution with depth (see below, Eq. 5), they are not completely independent parameters. For instance, a decrease in v (decreasing reactivity) can be compensated for by a simultaneous decrease in parameter a (increasing reactivity). Consequently, different a and v parameter couples might result in equally statistically satisfying fits between the simulated and observed profiles. These limitations can be alleviated by using comprehensive, multi-component observational datasets, because the inclusion of additional information adds further constraints.
However, such datasets are often not available. For instance, the first efforts to quantify a and v parameter values, and thus reactivity, on the basis of observations solely focused on downcore TOC profiles  (Boudreau and Ruddick, 1991). Such an approach was instrumental for the development of the RCM and for producing the first large-scale dataset of a and v. However, the increasing availability and accessibility of multi-species datasets allow for the use of additional information that is available across different sites. Because OM heterotrophic degradation is the driver behind biogeochemical dynamics in marine sediments, downcore profiles of TEAs (e. g. SO 2-F. S. Freitas et al. :  New insights into large-scale trends of apparent organic matter reactivity duce equivalent TOC depth profiles (Supplement Fig.
Emerging environmental patterns in OM reactivity Parameter a distributions exert the main control on the global variability in apparent OM reactivity. It also displays plausible values spanning several orders of magnitude (Fig. 4b , Table  6 ). Thus, our ability to quantify and predict benthic OM dynamics strongly depends on a general framework that allows constraint of parameter a when observational data are scarce or unavailable (e. g. global, future, and past model scenarios). Therefore, we further explore these links combining our newly generated dataset with those compiled from previously published RCM parameters (Fig. 6 ). Despite the large scatter, broad trends emerge that generally agree with previously proposed relationships k-ω  (Boudreau, 1997; Tromp et al. , 1995) , k-J OM,in  (Boudreau, 1997; Müller and Suess, 1979) , and a-ω (Boudreau and Ruddick, 1991) (Fig.
Control of OM reactivity on OM degradation rates and respiration pathways In agreement with previous findings  (Henrichs, 1992) , depthintegrated OM degradation rates (R TOC, total ; Eq. 7) show a strong correlation with OM fluxes (J OM,in ) (Fig. 7a , r 2 = 0. 97; p<0. 01; n = 14). In contrast, they do not reveal a strong link with apparent OM reactivity (i. e. parameter a), thus indicating that the magnitude of the OM deposition flux rather than its quality often exerts a first-order control on depth-integrated rates of OM degradation. In our dataset, apparent OM reactivity only becomes a dominant control on R TOC, total in depositional environments that receive exceptionally reactive OM (i. e. a < 10 1 years), such as the Arabian Sea region (Table  6 ). OM reactivity does exert an important impact on the relative contributions of metabolic pathways (Fig.
Finally, results also show that OM reactivity exerts a dominant control on the redox zonation of the sediment, the depth of the SMTZ, depth-integrated AOM rates, and benthic-pelagic exchange fluxes, indicating that these processes could serve as predictor variables for OM reactivity. In contrast, depth-integrated OM degradation rates are largely controlled by the magnitude rather than the quality of OM deposition to the sediment. Code and data availability. The model code is available at the GitHub repository (https://github. com/felipesalesdefreitas/BG_ 2020_435,  Freitas, 2021). The data are available in the cited literature in the paper. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-18-4651-2021-supplement. Author contributions. FSF, SA, and RDP designed this study.
PAP developed and implemented changes to model description (multi-G approximation of the RCM). SK, BBJ, JR, CR, ST, and HS provided porewater and sediment data to inform model experiments and contributed to discussing and interpreting the data and model results. FSF compiled data, ran all model simulations, and performed data-model integration. FSF wrote the manuscript with significant contributions from SA. All co-authors edited and approved the manuscript. Competing interests. The authors declare that they have no conflict of interest. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. Felipe S. Freitas was supported by a PhD scholarship from the  Science without Borders programme  ( Ciência sem Fronteiras ), sponsored by the  CAPES Foundation  within the  Ministry of Education, Brazil , grant number  9999. 009541/2013-06. https://doi. org/10. 5194/bg-18-4651-2021Biogeosciences, 18, 4651-4679, 2021.
) (e. g. Bowles et al. , 2014;  Jørgensen et al. , 2019b)  or reduced products of OM degradation (such as CH 4 and NH + 4 ) could be used as further constraints. However, data availability is often still limited; therefore, it is important to identify a minimum set of observational data that are widely available and comparably easy to measure. We tested the suitability of different artificial porewater datasets for the inverse determination of apparent OM reactivity. Using an artificially generated dataset (see Supplement Sect. S3), we ran the BRNS in the same fashion as we did for the site-specific data-model approach (see below). Our sensitivity analysis confirms that when TOC is considered as a single constraint, multiple pairs of a and v prohttps://doi. org/10. 5194/bg-18-4651-2021Biogeosciences, 18, 4651-4679, 2021.
J o u r n a l P r e -p r o o f A growing dataset of oxygen and carbon isotope compositions is available from carbonate-bearing paleosols worldwide, unevenly covering a long period of time from the Paleozoic to the Quaternary. From this dataset we selected the isotopic values obtained from Cretaceous to Quaternary paleosols. Analyzing those data from a global perspective, over large periods of time, we show that the isotopic signal reflects local and global conditions at a given, relatively short period of time, and that long-term trends in the isotopic data also exist that can be correlated to biological, topographic or climatic conditions.
Data collection In this study we use the data from 103 publications, corresponding to geological sites in both hemispheres covering large latitude ranges and distributed on the 5 continents (Fig. 1  and Supplementary Table  S1 ). Only data that were provided as numbers in tables were selected, avoiding collecting data from graphs. In order to get a homogeneous dataset, we selected oxygen and carbon isotope compositions solely from samples that could be assigned with high confidence to pedogenic carbonates (nodules, rhizocretions and pedogenic calcretes), and for which the formation age and the present-day geographic position are indicated. Those samples for which diagenetic alteration has been demonstrated or suspected by the authors of the original publication have been rejected as well as those developed in sediment hosting inherited carbonate grains when this is indicated in the publication.
In each individual study, when outliers exist but are not directly explained by factors such as diagenesis, inherited carbonate or potential analytical errors, they have been included in the dataset. Soil CO2 is generally a two-components mixture between atmospheric CO2 and CO2 derived from in situ oxidation of organic carbon (referred as soil respiration). Both soil J o u r n a l P r e -p r o o f Journal Pre-proof pCO2 and  13 C values thus vary with depth from atmospheric values at the surface to a constant soil value at depth, leading to a spread in individual isotopic ratios. Except for very low soil respiration rates, both parameters should be constant below depths of about 50 cm  (Cerling, 1984; Sheldon and Tabor, 2009). For studies dealing with Quaternary paleosols and indicating the sampling depth, the values from samples collected above a depth of 50 cm have not been selected.
The sampling depth for older paleosols is generally not constrained and all values have been considered. A total number of 12167 data have been isolated. Sheldon (2018)  recently proposed to use the difference between the carbon isotope composition of soil carbonate and that of soil organic matter  13 C =  13 Ccarb - 13 Corg to screen corresponding oxygen isotope values and reject those that do not correspond to soils in oxygen isotope equilibrium. However, most of the studies we used in this compilation do not include  13 Corg values. To preserve the homogeneity of our dataset we were thus unable to use this approach and did not screen the oxygen isotope values even when  13 Corg was available. The  13 C values are given relative to the Pee Dee Belemnite (PDB or VPDB for the equivalent Vienna standard).
This 3‰ difference, which might explain the wide distribution in the Neogene data, also matches the 3‰ increase in the  18 O values of deep-sea foraminifera during the same period, generally considered as reflecting the development of polar ice-caps and the cooling of deep-sea waters  (Lear et al. , 2000; Zachos et al. , 2001; Billups and Schrag, 2002)  (Fig. 2 ). A notable feature of the Quaternary dataset is the occurrence of tens of  18 O values (above + 30‰).
J o u r n a l P r e -p r o o f 1 4 As expected from theoretical models (e. g. Cerling and Quade, 1993) , the Quaternary mean  18 O value of each individual dataset shows a weak negative correlation with absolute latitude within the documented range of 4°-45° (Fig. 5a ). Between 25° and 40° the large spread in the data is probably related to local variations in evaporation and rainfall following the topographic factors described above. However, the theoretical relationship between the  18 O values and latitude is only observed in the Quaternary dataset, not for the other periods. The Quaternary mean  13 C values show a positive correlation with latitude between 4° and 30° possibly linked to the increase in aridity and relative proportion of C3 and C4 plants in the ecosystems (Fig. 5b ). Actually, altitude, by influencing the local climate (especially the mean temperature of the warmest months), also plays a role in the competition between C4 and C3 plants  (Cotton et al.
, 2014; Hyland et al. , 2019). Above 30° latitude, the mean  13 C values in the Quaternary datasets are largely spread, again probably in response to the impact of complex topography on the ecosystems and soil conditions. Correlations, positive or negative, exist in the  13 C vs.  18 O plot of individual datasets (Fig. 6a, b ). Positive correlations are well documented in the Quaternary and to a lesser extent in the Neogene dataset (see for example the data of  Salomons et al. (1978) ;  Cerling and Hay (1986)  or  Potts et al. (2009)  for the Quaternary and  Latorre et al. (1997)  for the Neogene). These trends covering time span of several 10 kyrs to several Myrs are usually explained by climate and ecosystem changes through time: an increase in aridity leads to increased evaporation (inducing higher  18 O values), soil hydric stress (inducing higher  13 C values) and favors C4 plants (again increasing the  13 C values).
Negative correlations are observed in the Quaternary (Yang  Wang and Shu-Hui Zheng, 1989) , possibly in the Neogene  (Charreau et al. , 2012)  and to a larger extend in the Paleogene  (Koch et al. , 1995; Alonso-Zarza and Arenas, 2004 ) (Fig. 6c-e ). Few authors J o u r n a l P r e -p r o o f 5 discussed such negative correlations that remain poorly explored. Only (Alonso-Zarza and  Arenas, 2004)  proposed that it could be controlled by climate: the driest periods would also be cooler, leading to high  13 C values, associated to a limited variation in  18 O compared to wetter, warmer periods. Finally, some of the Quaternary  (Alam et al. , 1997; Alomar, 2001)  and Neogene datasets  (Behrensmeyer et al. , 2007)  show a wide range in  13 C values associated to a narrow  18 O range (Fig. 6f, g ).
Conclusions The analysis presented here is based on the oxygen and carbon isotope data of pedogenic carbonates of the Cretaceous up to the Quaternary. We show that pedogenic carbonates record long-term, global isotopic signals, complementary to the record of oceanic carbonates. For example, whereas the influence of polar ice-caps from the Late Paleogene (and possibly during the Upper Cretaceous) is recorded in the oceanic  18 O signal, the effects of global continental hypsometry are not. In that respect, continental topography and associated enhanced aridity became a major forcing parameter during the Neogene and Quaternary periods, creating regional-to local-scale climatic conditions leading in turn to contrasted ecosystems. This complex climatic and biologic pattern, associated to the rise of C4 plants during the Neogene, seems to have led to uniquely complex soil carbonates  18 O and  13 C patterns.
Acknowledgements J o u r n a l P r e -p r o o f 6 We would like to thank  Ch. Lecuyer  for helpful discussions,  A. Kaakinen  and  J. Andrews  for providing access to datasets and two reviewers for their very helpful comments and suggestions. Funding for authors salaries was from  Centre National de la Recherche Scientifique  for  M. Jolivet  and University Rennes 1 for  Ph. Boulvais. 0 Behrensmeyer  et al. (2007) showing a wide range of  13 C values associated to a narrow spread in  18 O values.
Application to Tecator Data In this paragraph, we will apply the PLGSIMF model for Tecator data, popularly known in the functional data analysis. This data can be downloaded from the following link http://lib. stat. cmu. edu/datasets/tecator (accessed on 1 August 2021). For more details, see Ferraty and Vieu  [7]. Given 215 finely chopped pieces of meat, Tecator's data contain their corresponding fat contents (Y i , i = 1,. , 215), near-infrared absorbance spectra (Z i , i = 1,. , 215) observed on 100 equally wavelengths in the range 850-1050 nm, the protein content X 1,i and the moisture content X 2,i. We are trying to predict the fat content of the finely chopped meat samples. The following figure (Figure  5 ) shows the absorbance curves. We divide the sample randomly into two sub-samples: the training I 1 of size 160 and the test I 2 of size 55.
Non-Intrusive Load Monitoring (NILM) refers to the analysis of the aggregated current and voltage measurements of Home Electrical Appliances (HEAs) recorded by the house electrical panel. Such methods aim to identify each HEA for a better control of the energy consumption and for future smart grid applications. Here, we are interested in an event-based NILM pipeline, and particularly in the HEAs' recognition step. This paper focuses on the selection of relevant and understandable features for efficiently discriminating distinct HEAs. Our contributions are manifold. First, we introduce a new publicly available annotated dataset of individual HEAs described by a large set of electrical features computed from current and voltage measurements in steady-state conditions. Second, we investigate through a comparative evaluation a large number of new methods resulting from the combination of different feature selection techniques with several classification algorithms.
To this end, we also investigate an original feature selection method based on a deep neural network architecture. Then, through a machine learning framework, we study the benefits of these methods for improving Home Electrical Appliance (HEA) identification in a supervised classification scenario. Finally, we introduce new transfer learning results, which confirm the relevance and the robustness of the selected features learned from our proposed dataset when they are transferred to a larger dataset. As a result, the best investigated methods outperform the previous state-of-the-art results and reach a maximum recognition accuracy above 99% on the PLAID evaluation dataset.
The goal is to show the benefits provided by FS in terms of HEAs' classification performance and interpretability, and for enhancing the generalization capability by reducing overfitting of the trained models. Thus, transfer learning  [21] , which deals with the generalization capability of the selected features for different datasets, is also an important NILM challenge that is investigated in this study. This paper is organized as follows. In Section 2, the addressed problem is formulated. In Section 3, we introduce the two HEAs' datasets considered in this study. In particular, we present a novel dataset of HEAs' current and voltage measurements in steady-state conditions, and we detail the electrical features computed from these measurements. In Section 4, several FS methods are presented.
Problem Statement 2. 1. Supervised HEA Identification From the values of a set of features describing a unique HEA signature, we aim at identifying the closest pre-registered HEA, referred to (according to the machine learning literature) as a class. To this end, we use a supervised machine learning framework trained on an annotated dataset. The overall HEAs' recognition problem can be illustrated in the flowchart of Figure  1 , which also includes references to the paper sections where each method is detailed. During the training step, several features (or descriptors) deduced from voltage and current HEAs' measurements are first computed and used to derive a discriminative model for each HEA. Then, the most discriminant features are automatically selected to obtain the optimal classification accuracy.