dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). In this study, we perform a careful analysis of the two outputs of the VBB seismometer signals  (InSight Mars SEIS Data Service, 2019). These two outputs, the POS long period channel and the VEL broadband channel, are sensitive to the same accelerometric signal, but with a different transfer function and more importantly, with different and non-coherent self noises. See more in  Lognonné et al. (2019). We first show that normal mode search based on spectra analysis alone cannot provide meaningful normal mode identification, due to the low signal-to-noise ratio. As an alternative, we use a phasor walkout analysis and identify a set of 60 frequencies for which the phasor walkout is structured in the 3-12 mHz bandwidth. These stable oscillatory patterns are interpreted as Martian normal modes.
, 2020; Scholz et al. , 2020)  to provide glitch-reduced datasets. Here, an additional effort to remove glitches was performed on the 2 sps data following  Lognonné et al. (2020)  to obtain the cleanest possible data set at long periods. Glitches identified on the VEL channel have also been removed from the POS channel. Figure  S1  in Supporting Information S1 shows the lowest noise spectra obtained from raw data and from deglitched data with different deglitching threshold values. These spectra are computed using 12 hr of data between 14:00 LMST on sol 1222 and 02:00 LMST on sol 1223. Crustal Rayleigh waves, with group velocity of 2. 88 km/s  (Panning et al. , 2023) , will orbit about 6 times while 200 s mantle waves will orbit about 8 times due to larger group velocity of 3. 90 km/s (Figure  S2  in Supporting Information S1).
5 and 12. 5 mHz. PCC lag-time window is 100,000 s (≃28 hr), corresponding to 18. 3 orbits for 200 s surface wave. Finally, the spectral estimation of the PCC is performed with multi-tapers  (Park & Lindberg, 1987). This method employs a small set of orthogonal data tapers rather than one single taper to weight the data and to finally construct a high-resolution power spectrum with minimum spectral leakage. Figure  1  shows the spectral representation of the deglitched recordings from the vertical POS and VEL channels. The deglitching threshold is 24% and the spectra were computed using the multi-taper and PCC approach. Several maxima in the spectra are observed. Although the POS (blue line) and VEL (red line) spectra look different, we observe that most of the time, both spectra contain maxima at the same frequencies.
The strength of the MHum depends on both the atmospheric sources and of the strength of atmospheric coupling. As shown by  Lognonné and Johnson (2015) , the strength of atmospheric coupling is modulated at long period by resonances with the atmosphere found with about a 1. 1 mHz spacing and with up to a factor 5 of differences between the maxima and minima of coupling. Therefore, the MHum is most likely not exciting the normal modes in the same way, which might be a first explanation for the non-systematic presence of MHum at the Mars normal modes frequencies excited by S1222a. More analysis is planned with the full VBB data set to confirm these first observations.
1994 ) and defines the mixed layer depth. The domain extends from the east coast of South America to the west coast of Africa, from 30° S to 10° N at a resolution of 1/12° (Fig. 1 ), with 37 terrain-following vertical levels stretched near the surface. The bathymetry is derived from the GEBCO_08 30″ elevation database (http:// www. gebco. net) and the northern/southern open lateral boundary conditions are from the CARS2009 climatology  (Dunn 2009; Ridgway et al. 2002). Because river discharges are not explicitly considered, the model Sea Surface Salinity (SSS) is restored to the CARS2009 monthly climatological SSS with increased restoration coefficients in the coastal areas. The model time step is 900 s. For all the simulations performed, we store daily averages of the CROCO 2D-surface state variables (temperature, currents, and sea-level), while the 3D-variables and surface momentum/heat/freshwater flux are averaged every 3 days.
5) planetary boundary layer scheme (Nakanishi and Niino 2004); the Noah land-surface model; and the Mellor-Yamada-Nakanishi-Niino-Eddy-Diffusiv-ity-Mass-Flux surface layer scheme  (Nakanishi and Niino 2009; Olson et al. 2019). Cumulus are parameterized using the new simplified Arakawa-Schubert convection scheme from NCEP  (Han and Pan 2011; Lim et al. 2014). 3-h ERA5 data  (Hersbach et al. 2020 ) are used to initialize the model and to force it at the lateral open boundaries. The model time step is 90 s. For all the simulations carried-out with WRF, the association with the XIOS library allows for saving hourly means of 2D fields (10 m-winds, precipitations, and heat/water surface fluxes) and daily means of 3D fields (winds). The ocean-atmosphere coupling The Ocean Atmosphere Sea Ice Soil coupler version 4.
2006 ). Positive SSTA were also observed in the eastern tropical North Atlantic in the first trimester of 2021  (WMO No-1300 2022) , which also have the potential to influence the surface wind circulation in the WCEA  (Illig and Dewitte 2006). As shown in the ERA5 dataset (Fig. 7b ), there was indeed an easterly wind flow in the spring of 2021 that entered the western boundary of our coupled model (at 62° W), which was confined to the equatorial waveguide ([5° S-5° N]) between the ground and 500 hPa. It started in April 2021 and lasted throughout the fall of 2021, with a maximum of 3 m/s in June 2021 (not shown). And we showed in Sect. 3. 3 that the atmospheric response to the WRF ABCs has the potential to produce a relaxation of the Atlantic trade winds up to 0° W (Fig. 4a ). At the northern boundary (12° N), a southward inflow occurred from March to May 2021, confined to the upper layers above 500 hPa.
Forcing from the South At the southern boundary of our model domain in May 2021 (Fig. 7e ), strong winds blew southward, confined near the African coast and extending from the sea surface up to the top of the troposphere. Concurring with Fig. 3 , the southward anomalous winds started in March 2021   2 ) and analyzed in Sect. 4. The figure has been realized using the Ferret program (http:// ferret. pmel. noaa. gov/ Ferret/) and disappeared in July 2021, with a maximum surface wind outflow (> 1 m/s) in June 2021. On the western edge of the South Atlantic, counterbalancing northerly winds form the western side of a long-lasting anomalous lowpressure system (see Figs. 3  and 7e ). Results from the forced sensitivity experiments already suggested that this atmospheric circulation is detrimental to the dynamics of the 2021 coastal warming south of 16° S (Sect.
Acknowledgements We would like to thank  Lionel Renault  ( IRD/ LEGOS ) for his help in choosing the atmospheric model parameterizations and for fruitful discussions on coupled processes. This study has been carried out in the framework of the  EU H2020   TRIATLAS  project under grant agreement  817578. Marie-Lou Bachèlery  has received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  for the project  BENGUP  under the  Marie Skłodowska-Curie  grant agreement  ID 101025655. Model computations were performed on CALMIP computer at  University Paul Sabatier (Toulouse, France , CALMIP, project 19002). CROCO model grid, forcing, and initial conditions were built using the CROCO_Tools software (Penven et al. 2008). CROCO and CROCO_TOOLS can be downloaded from https:// www.
Symbols as for a). Figures are created with the software Ocean Data View (ODV)  (Schlitzer, 2015). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article. ) 1996). This 3754-cm-long core consists of biogenic sediments rich in coccoliths and foraminifera  (Lancelot and Shipboard Scientific Party, 1997). This core was dated using radiocarbon dating on the planktic foraminifera Globigerinoides ruber and tuning the benthic foraminiferal Cibicidoides wuellerstorfi δ 18 O record to the benthic LR04 global stack  (Lisiecki and Raymo, 2005) (Figs. A. 2 and A. 3 ). The age model was constructed using 28 tie points, 7 14 C dates and 21 benthic δ 18 O tie points (Table  B. 2). In the uppermost part of the core (0-480 cm) (Fig. A.
The biological and physical processes controlling ocean carbon storage might be modulated by orbital forcing. To evaluate the influence of orbital cycles on carbon storage and related ice-sheets dynamics, spectral and wavelet analyses were performed using the software PAST  (Hammer et al. , 2001). The corrected benthic δ 13 C record of core MD96-2077 shows significant spectral peaks with periodicities of 100 kyr (eccentricity) and 23 kyr (precession) for the whole studied interval (Fig. 7a ). Furthermore, higher carbon storage (lower corrected benthic δ 13 C) occurred at times of relatively lower eccentricity and obliquity within glacial periods (see A-F red rectangles in Fig. 8b, c,  and d ). These findings support that orbital forcing could have influenced the extension and reduction of the nutrient-rich deep waters, and related ocean carbon storage.
8d ). The effect of local productivity influences on this trend was estimated to be minor since the highest surface productivity (inferred from G. bulloides abundance  (Chen, 1996 [dataset] ), the related highest organic matter accumulation and preservation (based on TOC and alkenones in bulk sediment) and seafloor oxygen decrease (deduced by authigenic uranium content)  (Bard and Rickaby, 2009) , occurred at the end of glacial periods (Fig. A. 4). In contrast, corrected benthic δ 13 C values decreased across glacial inceptions (Fig. A. 4), revealing that carbon-rich deep waters arrived at the SW Indian at the beginning of glacial periods. Interglacial to glacial δ 13 C change was 0. 5, 1. 3, and 1. 6‰ for glacial MIS 14, 12, and 10, respectively. Therefore, carbon storage in the SW Indian Ocean increased gradually until MIS 12 and 10, which show extreme glacial conditions.
Acknowledgments We thank the editor  Dr. Fabienne Marret-Davies  for handling this manuscript. We are very thankful to two anonymous reviewers for their insightful comments that substantially improved this manuscript. JNPA was funded by the  INDEXCLIMA  project. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement  N • 840675. Nd isotopic analyses were measured on Neptune+ MC-ICPMS acquired in the frame of EQUIPEX ASTER-CEREGE project. We also thank the support of the  INSU-LMC14 national laboratory  for  ARTEMIS radiocarbon dating  for samples from core  MD96 -2052 and the  INSU-LEFE-CLIMOZA  project for stable isotope analyses from core  MD96-2052. Marta Garcia  for her technical support with ICP-MS Nd concentration measurements and  Hélène Mariot  for her careful maintenance of the clean laboratory are thanked.
We describe the spatiotemporal domain, applications, logistical and data requirements, and limitations to help guide future work on CWRs. Identifying the distribution of CWPs is the first step towards monitoring them and can be facilitated through a combination of the methods outlined below, alongside local and traditional 13652486, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/gcb. 16844 by Test, Wiley Online Library on [04/08/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License knowledge. CWRs are four-dimensional features, but tools used to assess their physical extent are often constrained to two or three dimensions and confined to small to moderate spatial scales (e.
Frequency and keyword associations analyses We calculated frequencies for all the terms in the documents and for the keywords. We also developed keyword associations using cosine similarity scores. Data matrices with sparsity percentages (i. e. , percentage of cells in a database table that are not populated) greater than 60% were considered too sparse to calculate associations. The cosine similarity scores range from 0 to 1. We applied the inverse document frequency (IDF) weight to the cosine similarity score. Lastly, we inspected word associations for all case studies except for the Rhône River case study because its data matrix was too sparse (sparsity of 68%) Results-main finding #1 Keywords related to CWRs (Table  S2 ) were not among the most frequent words in the analyzed documents (Tables  S3  and S4 ).
Enrollment in the IWQGE program is voluntary, and guidelines only encourage monitor- ing of physical and chemical stressors such as water temperature. The IWQGE uses remote sensing to monitor changes in surface area of waterbodies over time in aquatic ecosystems but does not monitor or protect specific habitats or biota of cultural, economic, or ecological importance, such as salmonids. The GEMS program, one of the largest global databases of in-situ measurements of water quality (Desbureaux et al. , 2022) , collects river water temperature from 72 countries, of which 38 have salmonids. The UNEP proposed benchmarks for "high integrity systems" to ensure no deviation from water temperature background values or identifying optimum temperature ranges of relevant species (United Nations Environment, 2017).
)and steelhead (O. mykiss) populations Protect trout from recreational angling in summer when they concentrate at cold tributary confluences Identify location & type of CWRs at the watershed scale Identify CWP location of river reaches Identify and characterize CWPs as a tool to support decision-making in the control of groundwater extraction for irrigation, to ensure the preservation of riverine ecological values, with a focus on fish Secondary (or additional) management goals Defining management question/goal (step 1-Figure 4) Identify steps and demonstrate methods for identifying, protecting, and restoring cold water refuges Protect and enhance CWR habitat Locate and classify CWPs Baseline CWPs for future monitoring Datasets Assessing data availability (step 2-Figure 4) Use data from regional water temperature network NorWeST to identify data needs Local water temperature monitoring and data on CWRs occupancy monitoring Airborne thermal infrared surveys of CWRs, CWRs classification table, water temperature monitoring Satellite thermal infrared from Landsat-The Enhanced Thematic Mapper Plus (EMT+) Drone-Based Thermal Infra-Red (TIR) imagery Physical monitoring Mapping and prioritization of area of interest (Figure 2, step 3-Figure 4) Temperature, bathymetry, dissolved oxygen (1 refuge only where high aquatic vegetation densities indicated potential for concern) Temperature and hydrodynamic data collected at a subset of the confluences Airborne thermal infrared (TIR) surveys, temperature loggers, water chemistry, drone flight revisits, ground-based thermal refuge validation, bathymetry, and drivers of confluence configuration Airborne TIR surveys, mainly diagnosis step 50 km baseline Drone TIR survey in April 2017 (end of Australian summer) to detect CWP and identify their drivers Biological monitoring Mapping and prioritization of area of interest (Figure 2, step 3-Figure 4) Temperature-and depth-sensitive radio-tags, visual surveys, PIT tag detections, dam passage counts (including quantification of migratory delay associated with refuge use) Yearly electrofishing and fish counts, species identification Electrofishing, thermal habitat selection, habitat suitability modeling, PIT-tags and visual surveys None None Physical modeling Evaluating modeling options and needs (Figure 2, step 4-Figure 4) Spatial stream network (SSN) models of temperature including shade and flow covariates; thermal plume modeling using COR-MIX None RivTemp and associated products, statistical models at sub-catchment level to understand drivers of CWRs Statistical models mainly to predict water temperature Statistical model to predict drivers of CWPs (Continues) framework Columbia Housatonic Restigouche Rhône Ovens Biological modeling Evaluating modeling options and needs (Figure 2, step 5-Figure 4) Spatially explicit bioenergetics model; thermal exposure modeling None Habitat selection and drivers of onset of aggregations None None Target species/group of species Assessing biological linkages (step 5-Figure 4) Summer steelhead (Oncorhynchus mykiss), fall Chinook salmon (O.
On December 2020 we first ran a bibliographic research on WOS looking for "Cu AND availab*AND soil AND TOPIC function". We then completed this research by examining the references cited in the articles found. We collected the available relationships for estimating Kf on the basis of soil pedo-geochemical characteristics and/or total Cu. We selected only relationships that were based on commonly collected soil pedo-geochemical characteristics, such as soil organic matter (OM) or soil organic carbon (OC), dissolved organic carbon (DOC), cationic exchange capacity   (Schulzweida, 2019)  to a spatial resolution of 0. 5° (equivalent to approximately 50 km). This was done to match the resolution of the land surface models that were used to estimate the runoff. The resulting runoff data is presented in section 2.
| Statistical analyses For all statistical analyses, locations represent the difference between coastal and inland environments, while sites represent the different sites or ponds prospected within each location. Descriptive summaries of these data are given in Appendix S1. All data analyses were performed using R 3. 6. 3 (R Core Team, 2020) and Rstudio v1. 1. 419. For individuals captured during the breeding season, we tested for the existence of assortative mating, either in coastal or inland populations, by running Pearson correlation tests between male and female body size (SVL). We tested for the effect of location (coastal or inland) on individuals' body size (SVL), body mass and body condition (BCI), separately for males and females. These effects were assessed by computing linear mixed models (LMMs, packages lme4  [Bates et al.
A picture was taken from above in order to measure the total length of the egg string using ImageJ software  (Schneider et al. , 2012). For each clutch, we randomly selected 6 segments of 10 cm long and individually counted the number of eggs within each segment. The mean number of eggs per 10-cm segment was calculated and used to assess fecundity (number of eggs) for each clutch based on the length of the egg strings. We also calculated egg density as the For individuals captured outside the breeding season, we tested for the effect of location (coastal or inland) on individuals' body size (SVL), body mass and body condition (BCI), separately for males and females. These effects were assessed by computing LMs with location as an explanatory covariate.
, 2016; Tornabene, Hossack, et al. , 2021) , as well as aldosterone (involved in ionic and water regulations in amphibians;  Hillyard et al. , 2008; Tornabene et al. , 2022) , may be key parameters to consider in order to investigate the mechanistic bases of the differences we found. Finally, although some of the data we collected may have included juvenile individuals (i. e. sampling outside the reproductive season), our data gathered on reproductive individuals clearly show that the pattern we found is attributable to smaller adult body size in coastal individuals. Interestingly, these data show that sexual maturity is attained at smaller body size in coastal individuals (i. e. 59 and 56 mm SVL, respectively, for the smallest female and male involved in an amplexus) as compared to inland individuals (i.
s -1 ). Mixes for qdPCR were prepared as follows: 3 µL PerfeCTa 2X qPCR ToughMix low ROX (QuantaBio), 0. 6 µL 20X GE sample loading reagent (Fluidigm), 0. 3 µL 20X primer stock prepared using 18 µM for each primer (forward and reverse) and 4 µM of probe, and 1. 8 µL of extracted DNA, completed to 6 µL with nuclease-free water. Quantitative digital PCR was run on qdPCR 37k IFC digital array microfluidic chips using a Biomark system (Standard BioTools). For amplification, the thermal profile constituted of a first step at 50 • C for 2 min and a second step at 95 • C for 10 min followed by 40 cycles at 95 • C for 15 sec and 60 sec at 60 • C (2 • C s -1 ). Data analysis was performed with the Fluidigm digital PCR analysis software v4. 1. 2. Mixes as well as thermal profiles used for the two PCR methods are also detailed in  [10].
Sequencing Data Analysis Raw data (fast5 files) were basecalled using guppy_basecaller v6. 0. 1 + 652ffd1 and the super high accuracy model including a q-score filter of 10. Data were demultiplexed using guppy_barcoder v6. 0. 1 + 652ffd1 and -trim-adapters and -compress-fastq parameters. Basecalled and demultiplexed data were processed using the STECmetadetector pipeline v0. 1. 2  [10]  (https://gitlab. com/bfr_bioinformatics/STECmetadetector, accessed on 26 July 2023). Extracted E. coli reads in fastq format were deposited in NCBI BioProject PRJNA982778. We assumed that the inoculated STEC strain was fully characterized when the two virulence genes stx and eae were co-located on the same contig. The figures were generated using ggplot2 v3. 4. 0, reshape2 v1. 4. 4, and patchwork v1. 1. 2 packages on R v4.
1. 2 and R studio v2021. 9. 0. 351  [16] [17] [18]. The O-antigen clusters for O2 and O50 differ by a maximum of 2 potential SNPs  [13, 19]  and are therefore considered identical and treated as a single molecular serogroup  [20]. However, wzy O2 and wzy O50 (1 SNP difference, which cannot be reliably differentiated using MinION sequencing) have separate entries in the database included in the STECmetadetector pipeline. On the contrary, there is a single entry for wzx O2/O50. In the mapping analyses, reads mapping on wzy O2 and wzy O50 were thus combined.
Characterization of the STEC Strain in Presence of Commensal E. coli Using STECmetadetector Two replicates of each condition and three replicates for the condition with equal levels of STEC:commensal post-enrichment (Mix 3) were sequenced using MinION sequencing, and data were analyzed using the STECmetadetector pipeline. The STECmetadetector pipeline includes an initial mapping step to detect the presence of multiple O-groups in the set of E. coli reads. Figure  1  represents the read mapping depth on O-groups (wzx and wzy genetic markers) for O26 and O2. Mapping results were in line with quantification results (qdPCR) showing a higher number of reads mapped to O26 compared to O2. When the O2 strain was quantified below 10 8 copies. mL -1 in the enriched samples (Mixes 1, 2, 4, and 5), only a few reads corresponding to O2 could be detected.
Data Availability Statement: The STECmetadetector pipeline is freely available at https://gitlab. com/bfr_bioinformatics/STECmetadetector, accessed on 26 July 2023). The extracted Escherichia coli reads from each sample were deposited on NCBI under BioProject PRJNA982778 available at http://www. ncbi. nlm. nih. gov/bioproject/982778, accessed on 26 July 2023.
2014 ). City centres are not systematically the hottest parts, depending on local shading and canyon effects  (Hart and Sailor 2009; Sun et al. 2009). The assessment of topography and land cover effects on air temperature space-time variability has been facilitated by the development of fine-scale digital terrain models and vectorial database describing land cover at high spatial resolutions. These effects vary over both annual and diurnal cycles (e. g. Fenner et al. 2014; Wicki and Parlow 2017; Nikoloudakis et al. 2020)  and are modulated by climatological and meteorological conditions  (Johnson et al. 1991; Alcoforado and Andrade 2006; Heusinkveld et al. 2014) , thereby suggesting potential additive or antagonist effects depending on the time of day and year. Furthermore, an accurate assessment of the drivers of urban air temperature variability requires to sample air temperature at high spatial resolution and over a long time period (e.