dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection This analysis included data from elite national teams competing from 2009 to 2021. All performances in World Triathlon races were collected: WTCS, World Championship (WChamp), Continental Championship (ConChamp), and Olympic Games (OG) on the MTR. Within 38 MTR races, 394 different teams have finished the race, representing 1,576 performances realized by 2,924 male and female athletes. The ITU race data are publicly available and extracted from the World Triathlon database (www. triathlon. org). All the data were downloaded using a script on R querying by a public API  (12).
Data analysis All race times were collected (swim, bike, run, and transition) to track the positions of each triathlete during the relays. Nonfinishers (DNF, DSQ) were considered to finish last, classified in the order of the teams' withdrawal from the race. Each racer is an iteration of a supersprint triathlon; they were coded as Legrelayer number-sport. The transition phases were excluded from the analysis because the position changes are an information redundancy with the previous phase. Indeed, in our dataset, all the transitions did not bring any change to the ranking between the entry and exit of the transition phase. The relay teams were divided into four groups according to their positions throughout the race (state), the first (TOP1), second and third (TOP2-3), fourth to eighth (TOP4-8) positions, and teams above the eighth place (overTOP8).
Little work has been done on the multivariate functional linear model which corresponds to the case p ≥ 2 and the H j 's are all function spaces for all j = 1,. , p. Up to our knowledge, the model has been first mentioned in the work of  Cardot et al. (2007)  under the name of multiple functional linear model. An estimator of β is defined with an iterative backfitting algorithm and applied to the ozone prediction dataset initially studied by  Aneiros-Pérez et al. (2004). Variable selection is performed by testing all the possible models and selecting the one minimising the prediction error over a test sample. Let us also mention the work of  Chiou et al. (2016)  who consider a multivariate linear regression model with functional output. They define a consistent and asymptotically normal estimator based on the multivariate functional principal components initially proposed by  Chiou et al.
(2016). We start in Section 2 with a discussion on the restricted eigenvalues assumption in functional spaces. We prove in Section 3 a sparsity oracle inequality for the empirical prediction error in the case of fixed or random design. In Section 4 a sparsity oracle inequality for the prediction error in the case of random design is proven under some assumptions on the design distribution. We also derive precise rates for the projected estimator. In Section 5, a computational algorithm, inspired by the glmnet algorithm  (Friedman et al. , 2010) , is defined for the estimator β λ , allowing to minimise Criterion (3) directly in the space H, without projecting the data. The estimation and support recovery properties of the estimators are studied in Section 6 on simulated dataset and applied to the prediction of energy use of appliances.
Each variable is measured every 10 minutes from 11th january, 2016, 5pm to 27th may, 2016, 6pm. The data is freely available on UCI Machine Learning Repository (archive. ics. uci. edu/ ml/datasets/Appliances+energy+prediction) and has been studied by  Candanedo et al. (2017). We refer to this article for a precise description of the experiment and a method to predict appliances energy consumption at a given time from the measurement of the other variables. Here, we focus on the prediction of the mean appliances energy consumption of one day from the measure of each variable the day before (from midnight to midnight). We then dispose of a dataset of size n = 136 with p = 24 functional covariates. Our variable of interest is the logarithm of the mean appliances consumption. In order to obtain better results, we divide the covariates by their range.
We also excluded the Arctic and southernmost biomes, which are seasonally covered by sea ice, due to fewer data available, and due to the complexity of isopycnal contours in those regions. The regional OUR analyses presented here were repeated 10 times, each time using all the data in one region and excluding all other data. Thus, we analyze 10 biogeochemically distinct regions that exclude coastal waters and most of the continental shelves. Combing all regions, from the base of the euphotic layer to the bottom, accounts for 1. 17 × 10 18 m 3 of seawater. Thus, 14. 3% of a total ocean volume of 1. 37 × 10 18 m 3 are not included in our analysis. The exclusion of high-latitude systems implies that our global estimates are conservative. We acknowledge that the choice of regions is subjective, and that other sets of biogeochemical regions have been defined for the mesopelagic realm  (Reygondeau et al.
For each density bin, we performed a linear fit of TOU versus seawater age using the MATLAB fitlm function and the built-in "robust regression" option that reduces outlier effects. From each fit, we extract the slope, standard error associated with the slope, and the p-value testing the null hypothesis of zero slopes, that is, that TOU and age are independent. The slope of the TOU-age linear relationship corresponds to OUR expressed in μmol kg -1 a -1. Only OUR resulting from a statistically significant relationship between TOU and age (p < 0. 05) were retained to reduce the risk of coastal or riverine water masses-or other allochthonous waters-locally altering the TOU and age signals. To test the robustness of our approach, we also replicated our entire analysis using simply GLODAPv2 oxygen concentrations ([O 2 ]) instead of TOU (Figure  S6  in Supporting Information S1).
Seawater-neutral density was absent from the DOC data set but was present in the GLODAPv2 data product. Thus, for each of the 10 regions, whose bounds roughly follow outcropping isopycnal contours, we fitted a linear regression model to predict GLODAPv2 neutral density as a function of GLODAPv2 absolute salinity and conservative temperature. In turn, in each region, the regression model was used to compute the neutral density associated with each DOC sample based on the sample's conservative temperature and absolute salinity, calculated using the TEOS-10 toolbox  (McDougall & Barker, 2011). As for TOU, the uncertainty associated with DOC is set to a conservative value of 10%. This uncertainty, as well as the uncertainty in seawater age, are propagated across 5000 Monte Carlo simulations into the final uncertainty of the DOC consumption rates.
POC Consumption Rates For the estimation of POC consumption rates, POC flux measurements from the sediment-trap data set of  Mouw et al. (2016)  were used. In each region, POC fluxes were sorted according to increasing depth and interpolated in the vertical using a cubic smoothing spline from the MATLAB curve-fitting toolbox (smoothing parameter p = 1 × 10 -6 , De  Boor, 1978). This allows us to turn discrete values into continuous estimates over depth and obtain regionally harmonized depth profiles of POC settling fluxes. These fluxes were used to compute POC consumption rates in three predefined depth zones, defined in Methods Section 2. 4. : (a) the twilight zone, (b) the midnight zone, and (c) the abyss. In each zone, the difference between the deepest and the shallowest interpolated flux is divided by the zone height, and the resulting consumption rate is then converted into μmol kg -1 a -1.
Seafloor and total OUR were horizontally integrated to obtain the vertical profiles plotted in Figure  11. In all regions, total OUR declines systematically with water depth, while seafloor oxygen consumption initially declines with water depth, but increases again because of ocean hypsometry (large parts of the ocean have depths between 3 and 6 km). Consequently, in all regions, except in the north subpolar Pacific, seafloor OUR can explain total OUR at depths below 2-4 km, depending on the ocean basin. This pattern is very similar to that reported by  Emerson and Hedges (2012)  and  Middelburg (2019)  based on different approaches and datasets. Based on sediment oxygen consumption data,  Middelburg (2019)  concluded that sediment respiration dominates below 3 km depth because of hypsometry, which amplifies the seafloor respiration signal occurring at these depths.
Integrated Budget We find a global respiration rate below the euphotic zone of 907 ± 165 Tmol O 2 a -1. Subtracting from this rate, the oxygen utilization rate at the seafloor (74 ± 8 Tmol O 2 a -1 ) gives an aphotic water-column respiration rate of 833 ± 165 Tmol O 2 a -1. Assuming a Redfield O 2 :C ratio for aerobic respiration  (1. 3) , this translates to a respiration rate of 640 ± 127 Tmol C a -1. This respiration rate is statistically indistinguishable from the global water-column organic carbon consumption rate of 727 ± 182 Tmol C a -1 (511 ± 179 Tmol of POC and 216 ± 35 Tmol of DOC) that we derived in this study using independent datasets. Moreover, part of the respiration occurs anaerobically using nitrate. Global denitrification rates are about 170-240 Tg N a -1 (DeVries  et al. , 2013; Gruber, 2008), corresponding to 10-14 Tmol C a -1 , that is, within the uncertainty.
Data Availability Statement The TTD ages  (Jeansson et al. , 2021)  are made available as GLODAPv2 affiliated data on the NOAA Ocean Carbon Data System website at https://www. ncei. noaa. gov/access/ocean-carbon-data-system/oceans/ndp_108/ ndp108. html. Seawater chemistry data are available from the GLODAPv2. 2016 in  Key et al. (2015) ,  Lauvset et al. (2016)  and  Olsen et al. (2016) , sediment-trap data are available from  Mouw et al. (2016) , 14 C-derived ages are available from  Gebbie and Huybers (2012) , biome distributions are available from  Fay and McKinley (2014) , and the dissolved organic matter data set is available from  Hansell et al. (2021). Depth profiles of OUR and DOC consumption rates, a compilation of existing OUR values, as well as all data necessary to derive the main results from this study, are made available on Zenodo at https://zenodo.
The remaining high-quality reads were clustered into operational taxonomic units (OTUs) at a 97% similarity threshold  (Behnke et al. , 2011). After normalization of the dataset, OTUs were searched against the SILVA database (release 138, http://www. arb-silva. de/)  (Quast et al. , 2012)  using BLASTN  (Altschul et al. , 1990)  for taxonomic affiliation. Sequencing data have been submitted to the NCBI sequence read archive database (SRA accession: PRJNA980077).
Statistical analyses and graphical representations Dissimilarity indices of microbial assemblages were computed using the Bray-Curtis method as a dissimilarity index with square root OTU read abundance normalization using R studio with the "vegan" package  (Dixon, 2003). Hierarchical cluster analysis was performed with the Ward method using the "Cluster" package  (Maechler et al. , 2013). Results were then plotted in a dendrogram using "factoextra"  (Kassambara and Mundt, 2017)  and "RcolorBrewer"  (Neuwirth and Neuwirth, 2011)  packages. Nonmetric multidimensional scaling (NMDS) and permutational analysis of variance (PERMANOVA-Adonis) were performed using the vegan package  (Dixon, 2003). Post-hoc Adonis tests were achieved using the "pairwiseAdonis" package  (Teng et al. , 2018). Alpha-diversity estimators (diversity-Simpson and Shannon; richness-Chao 1; and dominance-Equitability) were calculated using the Past 4.
03 software  (Hammer et al. , 2001)  for each replicate of each treatment. Values were averaged for each treatment and distribution represented in boxplots using the "ggplot2" package. Possible significant differences in alpha-diversity estimators between conditions of each generation were evaluated with oneway ANOVA or Kruskal-Wallis one-way analysis of variance, depending on results of normality and homoscedasticity of variances with R software. A p-value under 0. 05 was considered statistically significant.
The identification of spikes (i. e. short and high variability in the measured signals due to very local emissions occurring in the proximity of a measurement site) is of interest when using continuous measurements of atmospheric greenhouse gases (GHGs) in different applications like the determination of long-term trends and/or spatial gradients, the inversion experiments devoted to the top-down quantification of GHG surface-atmosphere fluxes, the characterization of local emissions or the quality control of GHG measurements. In this work, we analysed the results provided by two automatic spike identification methods (i. e. the standard deviation of the background -SD and the robust extraction of baseline signal -REBS) for a 2-year dataset of 1-minute in-situ observations of CO2, CH4 and CO at ten different atmospheric sites spanning different environmental conditions (remote, continental, urban).
After this quality control step and a final common review within the ICOS Atmosphere Monitoring Station Assembly, a fully quality controlled Level-2 dataset is released  (ICOS RI, 2022). Common instructions about data validation were defined within ICOS and data have to be invalidated by the site PI only for objective and testified reasons (such an instrument failures or contamination due to maintenance works). Very local emissions that might occasionally occur in the proximity of the measurement sites and that can produce short but intense variability in the measured GHG signals (i. e. "spikes") can jeopardize the full value of these atmospheric measurements when e. g. using those to constrain the quantification of GHG atmospheric fluxes at regional and global scales. Here, "local" refers to emissions occurring in a range of a few km from the site which cause positive short-term spikes with maximum duration from minutes to less than a few hours.
Pic du Midi (PDM, France) This high-mountain site (42. 56° N, 0. 80° E) is located on the NW side of the Pyrenees in SW of France. Due to the high elevation (2877 m a. s. l. ), PDM is affected by air masses from the free troposphere from the Atlantic Ocean. As other mountain sites, PDM can be affected by upslope winds and valley wind circulations especially in summer and early autumn, bringing air from the boundary layer of southwest France (covered by intensive croplands and forests, e. g. Fu et al. , 2016). In 2015, a field campaign was carried out at PDM to investigate the impact of a small sewage treatment facility to the atmospheric CH4 observations  (El Yazidi et al. , 2018). The same dataset was considered in this work to assess the efficacy of the automatic spike detection methods (Section 3.
Impact of the spike detections on hourly mean values Because ICOS atmospheric data are typically provided to external users as hourly mean values, it was important to document how the application of the two de-spiking methods impacted the dataset of hourly mean values generated by the temporal aggregation of the 1-minute data. To investigate the potential impact of the de-spiking methods to the 1-hour average values of CO2, CH4 and CO, we calculated the changes in the percentiles of the original data distribution after the application of the de-spiking methods with the "standard" settings (Figure  4 ). For this analysis, only the data record for the highest sampling level was considered for IPR, KIT, PUI and SAC. Specifically, we calculated the arithmetic differences between the data distribution percentiles (5 th , 10 th 50 th , 90 th and 95 th ) obtained for the de-spiked and the original dataset (i.
Impact of the spike detections on averaged monthly values An important point was to investigate the effect of the de-spiking methods on the monthly mean values of CO2, CH4 and CO, which are often used to illustrate/determine the long-term variability and trends of these trace gases. To reach this goal, for each available time series, the differences of the monthly mean values between the de-spiked and the original dataset were calculated for each algorithm and for each setting (i. e. varying α and β values). When referring to the mole fractions, hereinafter, we used the terms "impact" or "significant" when the differences between de-spiked and original dataset exceeded the WMO network compatibility goals (i. e. ± 0. 1 ppm for CO2, ± 2 ppb for CH4 and CO; see World Meteorological Organization (2020).
For CO, significant deviations in respect to the original dataset were observed only for REBS with β = 1. To summarize, the remote sites (CMN, JFJ, ZSF, UTO) were less impacted by the de-spiking and the deviations of the despiked datasets in respect to the original ones were mostly within the WMO network compatibility goals. For the continental sites, larger deviations in respect to the original dataset were generally found after de-spiking with REBS, while significant deviations were observed for SD only for a limited number of sites/sampling heights.
Impact of the spike detections on diurnal cycles We investigated how much the de-spiking methods influenced the seasonal averaged diurnal cycles of CO2, CH4 and CO at the test sites. To this aim, for each available time series, we calculated the average diurnal cycles for the original as well as for the de-spiked dataset by using the different settings reported in the Section 2. 3 (for tall towers only the highest sampling heights were considered) for the individual seasons. Seasons were defined as December 2019 -February 2020 (DJF), March-May (MAM), June-August (JJA) and September-November (SON). When looking at the results of de-spiking for CO2 (Figure  8 ), this analysis revealed that the application of SD had no impact on the shape of the 24-hour mean cycles: the diurnal peaks detected for the original dataset were maintained for the despiked datasets.
According to the site PI, the diurnal peaks observed around 8:00 AM and 20:00 PM at IPR, which were significantly smoothed by REBS, were related to very local sources of CH4 due to the systematic venting of cattle farms located in the proximity of the site. Significant impacts by REBS were also evident at KIT and SAC for CH4. Nevertheless, after the application of REBS (β = 3), the winter diurnal cycle at KIT appeared more "noisy" in respect to the original dataset suggesting the possibility of spike over-detection (13% of data were identified as spikes for KIT at 200 m a. g. l. ). For CO (Figure  10 ), significant deviations in respect to the original dataset were only observed when REBS with β = 1 was considered.
Comparison between automatic and manual spike detections In this Section, for a subset of sites (IPR, PUI, UTO), we presented a comparison between the de-spiking operated by the methods and that made by the PIs of the sites. The test was carried out for a few months of observations (see Table  2 ). To have a sufficient number of spikes available for comparison, the subset of sites was selected to have at least 1% of spike occurrence with respect to the whole dataset. In this context, the site PIs proceeded in making a manual flagging of the data, by adopting the same criteria and methodologies used during the routine data quality control. The results from this manual flagging were compared with the automatic flagging made by SD and by REBS. In this exercise, we also considered the CH4 1-minute data recorded in July -August 2018 at Pic du Midi (PDM) that were analyzed by  El-Yazidi (2018).
Discussion and conclusions In this exercise, we considered a subset of different atmospheric sites (i. e. remote, continental, urban) for which SD and REBS spike detection methods were applied to 1-minute data of CO2, CH4 and CO. Sensitivity studies were performed in order to compare the impact of the two methods to the original datasets in terms of spike frequency, hourly and monthly mean values as well as seasonal diurnal cycles. Case studies were considered to test the ability of the automatic methods in identifying spikes. Finally, "blind" tests were executed to objectively compare with a dichotomous analysis the agreement between the spikes manually identified by the site PIs and by the automatic methods. One main outcome of this study was that REBS identified a larger number of spikes than SD and was less "site-sensitive" than SD for CO2 and CH4.
On average, considering all time series, REBS detected about 10-times more spikes than SD. This led to a larger impact of REBS on the monthly averaged values and on the seasonal averaged diurnal cycle of CO2 and CH4: in respect to the original dataset, the application of REBS (with β = (1, 3)) led to significant impacts (i. e. deviations larger than the WMO network compatibility goal) for all the not-remote sites. On the other hand, SD reported impacts only for selected continental sites. For CO, significant deviations in respect to the original dataset were observed only for REBS with β = 1. As shown by the analysis of the 1-hour datasets, both SD and REBS were able to narrow the original data distribution or to shift it towards lower values. The application of the automatic methods to the case studies showed that it was challenging to identify one common algorithm/configuration for all the considered sites.
Stimuli The stimuli consisted of 20 colour photographs of scenes (10 indoor and 10 outdoor scenes) and 20 colour photographs of objects (10 animated and 10 non-animated objects). The photographs were downloaded from Pixabay (https://pixabay. com/fr/), a Creative Commons Zero (CC0) licensed photo sharing site, and from the Google Images database by filtering for the CC0 licence. The stimuli were created using MATLAB R2019b (The Mathworks, Natick, MA). The original photographs of the scenes were first cropped to a square format and resized to 700 × 700 pixels. They were converted to 256level grey-scale images by averaging the values of the three colour channels for each pixel. The average luminance of each image was equalised to 0. 5 (for pixel intensity values between 0 and 1) and the average Root Mean Square (RMS) contrast value of all images (0.
1947) was applied to each image. For each scene image, we then constructed a 700 × 700 pixels disc containing the scene. The original photographs of the objects were also cropped to a square format and resized to 120 × 120 pixels which corresponded to the maximum size of objects (either in width or height). The objects were then isolated from their background by outlining them on GIMP software (The GIMP Development Team. (  2019 ), 2019) and applying transparent pixels to their background. They were then converted to greyscale images. The average luminance and RMS contrast of each object were also equalised to 0. 5 (for pixel intensity values between 0 and 1) and the average RMS contrast value of all images (0. 2117) was applied to each image. Each object was then blurred using 2D Gaussian filters (imgaussfilt function in Matlab) of increasing size (i.
Procedure The experiment was constructed using the Psychtoolbox  (Brainard, 1997; Pelli, 1997)  implemented in MATLAB R2019b (MathWorks, Natick, MA, USA). Stimuli were displayed on a 30 ′ monitor (DELL UL-TRASHARP) with a resolution of 2560 × 1600 pixels. Participants put their head on a chin rest 72 cm away from the screen so that the grey disc containing the object corresponded to an angular size of 5 • visual angle, the object corresponded to a maximal angular size of 2. 4 • visual angle and the circular scene corresponded to an angular size of 14 • visual angle. On each trial, two images containing the same object were displayed on the left (the Target) and right (the Sample) sides of the screen against a grey background (average luminance of 0. 5). To test the effect of visual signal reliability on the perceived sharpness of objects, the Target object appeared in the first half of the trials at a blur level of 18 (SD of Gaussian filter = 7.