dataesr/scientific-paragraphs-categorization huggingface dataset:
The TCCON site at Réunion Island has been operated by the  Royal Belgian Institute for Space Aeronomy  with financial support since 2014 by the  EU  project  ICOS-Inwire and the ministerial decree for ICOS  ( FR/35/IC1  to  FR/35/C6 ) and local activities supported by LACy /UMR8105  and by  OSU-R/UMS3365  - Université de La Réunion. Darwin and Wollongong TCCON stations are supported by  ARC  grants  DP160100598 ,  LE0668470 ,  DP140101552 ,  DP110103118  and  DP0879468  and  NASA  grants  NAG512247  and  NNG05GD07G. We thank  Robert J. Andres  for providing uncertainty estimates for CDIAC fossil fuel emission estimates. We thank the data providers of the SeaFlux ensemble for making their pCO 2 -based air-sea CO 2 fluxes publicly available. We are grateful for the leadership of  Annemarie Eldering  and  Mike Gunson  of the OCO-2 mission, whose hard work has made this dataset possible.
In the Japan Trench, pioneering studies documented SOC delivery to the trench by historical subduction zone mega-earthquakes  2, 6, 18  with various magnitudes and recurrences on centennial-millennial time scales  4  , however, their impacts on the cycling of the dissolved carbon in trench sediments remain largely unexplored. Here we present the IW DOC and DIC concentrations, carbon isotopic profiles (δ  13  C and  14  C ages) and headspace methane concentrations in combination of typical IW geochemical parameters from 12 sites along the Japan Trench axis (water depth >7. 5 km, Fig. 1a ) collected during the International Ocean Discovery Program (IODP) Expedition 386  19. Using this deep subsurface dataset from a hadal trench, we attempt to elucidate the accumulation and transformation of dissolved carbon in the hadal trenches, and to decipher the roles of tectonic events in the carbon cycles of these ultra-deep-water environments connected to subduction zones.
4b ). Volcanic ashes, biosiliceous-rich sediments, detrital silicate minerals and the abundant alkalinity along the Japan Trench (Fig. 1b ) are suggestive of a contribution from marine silicate weathering, which provides the alkalinity and cations needed for authigenic carbonate precipitation  44  (Supplementary Fig. 5 ). This deeper-subsurface carbonate formation serves as a 'carbon sink swap' from OC to methane, carbonates and eventually to graphite as more stable forms in the subduction zone system  45, 46. Although substantial storage of methane has been reported in the continental shelf and cold seep sediments  47  , its presence in trench sediments is not thoroughly evaluated. A comparison of the methane data from the IODP database (Supplementary Fig. 6 ) suggests that the methane storage in the Japan Trench may be comparable to typical methane-rich seafloor areas.
Acknowledgements This research used samples and data provided by the  International Ocean Discovery Program   (IODP)  that are under moratorium until 30 November 2023. After the moratorium period, further data will be accessible online and more samples will be made available on request (www. iodp. org/access-data-and-samples). We thank staffs for assisting with sample preparation and measurement, and  Nan Wang  for comments on the figures. Expedition 386: Japan Trench Paleoseismology was jointly funded by the  European Consortium for Ocean Research Drilling (ECORD) , the  U. S. Science Support Program , and the  IODP , with contributions and logistical support from the  Japan Agency for Marine-Earth Science and Technology (JAMSTEC). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the funders.
28 km north-east of the PDM. The two centers make up the Pyrenean Platform for the Observation of the Atmosphere (P2OA). Data measured at PDM and CRA were combined to form a 5-year hourly dataset of 23 meteorological variables notably: temperature, humidity, cloud cover, wind at several altitudes. The dataset was classified using hierarchical clustering, with the aim of grouping together the days which had similar meteorological characteristics. To complete the clustering, we computed several diagnostic tools, in order to provide additional information and study specific phenomena (foehn, precipitation, atmospheric vertical structure, and thermally driven circulations). This classification resulted in six clusters: three highly populated clusters which correspond to the most frequent meteorological conditions (fair weather, mixed weather and disturbed weather, respectively); a small cluster evidencing clear characteristics of winter northwesterly windstorms; and two small clusters characteristic of south foehn (south to southwesterly large-scale flow, associated with warm and dry downslope flow on the lee side of the chain).
It is a classification method that groups data vectors (in the multi-dimensional space of all considered variables) depending on their closeness (details given in Section 2. 2). Carried out on a dataset of meteorological variables, it will generate clusters with similar meteorological characteristics. Such clusters can be linked to weather regimes, and hierarchical clustering has thus been widely utilized for this goal (e. g. ,  Kalkstein et al. , 1987; Ng et al. , 2020; Hodgson and Phillips, 2021, among many other references). With the aim of characterizing the specific impact at the P2OA of the synoptic meteorological context, the day-today changes are relevant to drive the clustering, but not the seasonal variations (e. g. , of temperature) nor the variations related to the diurnal thermal cycle.
Multi-year trends present for some variables (e. g. , CO 2 ) are not in our scope either. Therefore, a pre-processing will be applied to the raw observation data to filter out those undesirable components and keep the day-to-day variations only. Details of the measurements and the database, the data processing, the clustering method and the diagnostic tools are provided in Section 2. Meteorological regimes obtained from the clustering are presented in Section 3, and compared with diagnostic tools designed to focus on specific meteorological phenomena (Section 4). Finally we will consider and compare the statistics of the atmospheric composition variables available at PDM in the different meteorological clusters (Section 5). Conclusions are drawn, and perspectives suggested, in the final Section 6.
Database and hourly dataset The time frame of the present study runs from the 1st of January 2015 to the 31st of December 2019. The period was chosen to optimize data coverage for a panel of atmospheric measurements at both P2OA sites. All instruments used in our study, with technical details and the output variables are listed in Table  1. The entire P2OA dataset is described in  Lothon et al. (2023). We adopted the Coordinated Universal Time (UTC) for the whole study, because this time standard is almost the same as local solar time, since PDM is at longitude 0. 14 • E). As the data was collected at different time resolutions (Table  1 ), a first dataset was built on a synchronized hourly basis. Thus, the values provided for a given timestamp (e. g. , 27 May 2018, 08:00:00 UTC) represent averages of any data available in the one-hour interval beginning at this timestamp (08:00:00-08:59:59).
Even though the hierarchical clustering detailed below in Section 2. 2 is based on a final daily data set, building an intermediary hourly data set was needed for some of the diagnostic tools (Section 2. 3) working on an hourly basis -for example, the detection of diurnal cycles of wind or chemical concentrations. We used an extensive set of meteorological data recorded routinely at the PDM (2877 m a. s. l. ) and the CRA (600 m a. s. l. ) stations (data available online at https://p2oa. aeris-data. fr/) which included temperature, relative humidity and pressure measured by standard weather stations, cloud occurrence above the CRA and wind measured at different levels above the ground up to the mid-troposphere, as detailed here. At CRA, a 60 m tower provides meteorological measurements at both low and fast frequency.
We used the rain gauge at CRA for precipitation estimationss. Finally, surface energy and sensible and latent heat flux, deduced from the high-rate measurements at 30 m, were considered, to take account of surface/atmosphere interactions. To study the impact on the atmospheric composition measured at PDM, we considered the measurements of atmospheric composition (CH 4 , CO 2 , CO, O 3 ) and aerosol particle numbers (  (Hulin et al. , 2019, and  references therein for details on the instruments). Note that the CO data series used here is a composite of data from two instruments: an IR absorption analyser and a cavity-ringdown spectrometer, Table1). These were needed in order to upgrade the data coverage over the studied period at a satisfactory level. Radon volume activity has also been included in the present dataset even though the radon monitor has only been in operation only since October 2017.
Daily dataset suitable for separating synoptic weather regimes As discussed in the introduction (Section 1), the focus of this study is on the synoptic weather regimes at the P2OA and their impact on observations. The typical timescale for synoptic weather changes is a few days. Therefore, from the basic hourly dataset detailed in the former paragraph, we built a final daily data set composed of 1826 days, and where (i) the diurnal variations have been neutralized by averaging on a daily base; (ii) multi-annual and seasonal trends were characterized by means of a non-linear least-square regression, then removed. The regression function contains a linear part in order to model the long-term variation, and a 1-year periodic part with sinusoidal components (up to the fourth harmonic) to model the seasonal variability.
For this reason, we chose the linkage method for our study as we needed minimum variance within the cluster (well-defined weather regimes). With our dataset, however, single and average linkage resulted in one very large group and many singleday groups. Only the complete linkage method provided groups with more balanced populations, and hence this method was chosen for this study. Meteorological studies using hierarchical clustering use a different approach than in ours. They tend to use a Principal Component Analysis (PCA) on their input dataset. This analysis counteracts the inter-dependence of their input variables. This inter-dependence exists when studies focus on a zone with different measurement sites with few variables (e. g. Degaetano (1996) ,  Bravo et al. (2012) , Pineda-Martínez and Carbajal (  2017 )).
Diagnostic tools What we call diagnostic tools in this study are additional indicators computed mostly from data of our hourly or daily datasets, but in some cases also from additional observations (rain gauge) or an extra data source (NCEP meteorological model). Our main motivation is to further analyse the groups emerging from the hierarchical clustering, with a focus on specific atmospheric properties (e. g. , vertical structure) or phenomena (e. g. , foehn). All the diagnostic tools are summarized in Table  3. They have been computed either on an hourly or daily basis (depending on the need), and separated into three thematic groups: atmospheric vertical structure, thermally driven circulations, and foehn effect.
The latter two methods result in daily boolean flags which show whether or not a thermally driven circulation is detected for the current day. Finally, Method 3 consists of ranking days of the dataset depending on the influence of anabatic transport on the water vapour content measured in situ at PDM. This influence can be quantified using the amplitude of the diurnal cycle of specific humidity as a proxy. The larger this amplitude, the more efficient the anabatic transport of humid air from the valleys to PDM. This   2014 ) as an alternative. In our case, specific humidity and radon data are simultaneously available from late 2017 to the end of 2019, a period over which we checked that rankings based on both variables provided consistent results (not shown). In the following, we therefore only consider the ranking based on specific humidity, as humidity data were available at PDM for the whole timeframe of the study.
This pressure drag increases with the intensity of the foehn  (Lothon et al. (2003) ,  Drobinski et al. (2007) ). To compute this diagnostic tool, pressure data was needed from the Spanish side. We used mean sea level pressure data every 6 hours from the NCEP global reanalysis (https://rda. ucar. edu/datasets/ds083. 2/#!description), taken at the nearest grid point from Monzon (130 km south of CRA). For pressure on the French side, we had two possibilities: the actual pressure measured at CRA (reduced to msl) vs. the pressure data from NCEP reanalysis at the closest grid point. Higher differences were found with the measured pressure (suggesting that NCEP reanalysis could underestimate the foehn intensity due to the smoother terrain in the model. ), therefore we retained the measured pressure at CRA for the calculation.
The attribution is also consistent with the seasonality of Clusters 5 and 6 (Fig. 8 ), as the foehn is a phenomenon that mainly occurs in spring and autumn (according to studies conducted in Alpine regions, for example,  Bouët (1972)  and  Richner and Gutermann (2007) ). We found no literature reference on the foehn climatology in the Pyrenees). In the next section, using diagnostic tools specifically built to detect or characterize foehn events, we will check whether these two clusters contain the most Foehn events. 4. Synthetic table of the results of the study. For the chemical variables and weather, + stands for "above the median of the whole dataset", -stands for "below the median of the whole dataset" and / stands for "close to the median". For example, in Cluster 1, for CO2, -means that most of the CO2 anomaly distribution is lower than the full dataset median.
In fact, the drier and warmer ait of the foehn strongly favours the evaporation of soil moisture. Strong winds in cluster 4 also favor evaporation, even if this is less than in the dry foehn clusters. This could explain the smaller positive anomaly of LE in cluster 4. A positive anomaly of the same magnitude is found in Cluster 1, but is associated with the solar heating of the surface. The distribution within clusters for the Z i estimation (CBL height) may be affected by how the estimation is made. To get a Z i estimation of good quality, a well formed CBL is needed capped by a more stable and laminar atmosphere, which is generally not the case during disturbed weather events. This induces a difference of data availability of Z i estimations between clusters. By comparing the availability of the UHF and of the estimation, we were able to compute the percentage of days, for each class, with the Z i not defined.
Thermally driven circulations The 3 methods by  Hulin et al. (2019)  were designed to detect thermally-induced flows at different scales and locations: (1) the altitude return flow of the plain-to-mountain pumping, (2) the surface breeze at CRA and (3) local anabatic influence detected in situ at PDM (respectively referred to as Methods 1-3). These methods have been applied to our data set and the results analysed by clusters (Table  5 ). Hulin et al. (2019)  evidenced that for a given day in their database, the meteorological conditions did not always meet (or miss) the criteria of all 3 methods at the same time. They concluded that these different types of thermally-induced flows are not systematically concurrent and may occur independently from each other. This may explain why, in our case (Table  5 ), the percentages obtained for a given cluster differ between methods.
These events in Cluster 2 appear in Fig. 5b  as the strongest winds in the SW quadrant. This means that the unsupervised clustering used here was not able to gather all foehn days in specific clusters. This could partly be explained by the fact that those days also correspond to larger daily rainfall relative to the rest of the days in Cluster 2 (not shown). Thus, they correspond to the situation (mentioned in Section 1) of unstable southwesterly flows, with occurrence of storms. The very high proportion of foehn days in clusters 5 and 6 reveal that the most intense foehn events have a characteristic signature in the 23 meteorological variables listed in Table  2. In Cluster 6, there is one day not flagged as foehn simply because the VHF data point is missing this day. For Cluster 5, 10 days are not flagged as foehn with 4 because of missing data.
Summary and conclusions The present study proposes a non-supervised classification of 5 years of a basic set meteorological observation data collected at both sites of the P2OA. CRA is the foothill site and PDM the mountain top site. Prior to this study, the diurnal and seasonal components of the time series, as well as the multi-year trends (when present), were filtered out in order to isolate the day-to-day weather changes. The aim of this pre-processing and the subsequent classification is thus to form clusters of observation days with contrasting charateristics of the local meteorology, which could be related to synoptic weather regimes. Then, the statistical distributions of those data, but also of secondary diagnostic data tools, derived from the basic dataset or complementary observations, as well as atmospheric composition data at PDM, are analysed by clusters.
However, some results remain unexpected (less ozone in Cluster 6 than 5; no evidence of transported Saharan dust), and tracking the air masses back to their source region remains necessary to give a consistent interpretation for all species. Applied to a basic set of preprocessed meteorological data, the hierarchical clustering provided here days ensembles where the different variables are consistent with expected weather types. No contradictory information emerged, either between variables within a given cluster, or between clusters. The diagnostic data products did not contradict, but on the contrary brought further support to, the links established between the clusters and synoptic weather regimes, especially regarding the foehn clusters 5 and 6, and allowed us to paint more consistent and comprehensive portraits of the clusters.
Hence we can conclude that hierarchical clustering of the local meteorological data may be a valid and simple approach to characterize the meteorology of an atmospheric observatory -even in complex terrain -and its influence on the in-situ atmospheric composition. This non-supervised approach, which requires no external data and no a-priori knowledge of the local meteorology, could thus be easily carried out at other observation sites. A question that may arise, however, is whether the approach would be as fruitful in sites with fewer meteorological observations, especially in the absence of observed wind profiles. Further study could be to conduct sensitivity tests with our dataset in order to check the robustness of the obtained meteorological regimes when part of the observations are removed (e.
g. , keeping near-surface wind data only, which are much more widespread than wind profile data). Once proved robust for a few-year-dataset, one application of this approach could be to study the development of the regimes over a long period in order to detect the impact of climate change on the occurrence and nature of weather regimes. As a non-supervised approach however, hierarchical clustering may have limited ability to isolate a-priori known specific phenomena. An illustration of this in our study, is foehn, because most lee-wave events were found in Cluster 2 which was not specific to foehn. In order to make these foehn events fall into the specific foehn clusters 5 or 6, one could try to modify the metrics used to compute distances between the data points, giving more weight to variables known to be linked to foehn (the southerly wind component for example).
An isolate of a bacterium recovered from an endometrial biopsy failed to be identified by MALDI-TOF mass spectrometry and was subjected to 16S rRNA sequencing. The obtained sequence was compared by BLASTn against the NCBI database, which revealed that the most closely related species was Cellulomonas hominis and Cellulomonas pakistanensis, with 98. 85% and 98. 45% identity, respectively. Phenotypic characterisation and genome sequencing were performed. The isolate was facultative anaerobic, gram-positive, motile, non-spore forming, and rod-shaped. Cell wall fatty acid profiling revealed that 12-methyl-tetradecanoic acid was the most abundant fatty acid (36%). The genome size was 4. 25 Mbp with a G + C content of 74. 8 mol%. Genomic comparison of species closely related to this strain showed that all digital DNA-DNA hybridisation (dDDH) and mean orthologous nucleotide identity (OrthoANI) values were below published species thresholds (70% and 95-96%, respectively).
Ethical approval, sampling, and isolation of the strain An endometrial biopsy sampled for diagnostic purposes was sent to the clinical microbiology laboratory of Marseille Public University Hospitals (AP-HM, France) where it was retrospectively analysed using a culturomics strategy, as permitted by French law (Article L. 1211-2 of the French Code on Public Health). The patient was informed of the possible reuse for research purposes of her sample and the personal data collected from her during her care. She could oppose this by reporting it to the data protection officer at the AP-HM. The patient did not express any objection. The personal data that were used for analysis were anonymised. Our independent local ethics committee (Agreement No. 2022-009) approved the clearance of ERC and compliance with data protection legislation.
MALDI-TOF identification, 16S rRNA identification, and phylogenetic analysis Strain Marseille-Q7820 was identified using a Microflex LT MALDI-TOF mass spectrometer (Bruker Daltonics, Bremen, Germany) following the protocol described by  Seng et al. (2013). The MALDI BioTyper software (version 2. 0, Bruker) was used to analyse the spectra of the strain by standard pattern matching with default parameter settings. The resulting scores were interpreted as previously described by  Hadjadj et al. (2016).
13. 1 software, excluding scaffolds below 800 bp and depth values below 25% of the average depths. The obtained genome was annotated using Prokka 1. 14. 5  (Seemann 2014; Zgheib et al. 2020 ) and compared with those of closely related species. The 16S rRNA sequence was also analysed. The 16S rRNA sequences of the Marseille-Q7820 strain and their closely related species were aligned, and a phylogenetic tree was constructed with 1000 bootstrap replicates, based on the Neighbor-Joining method  (Saitou and Nei 1987)  and the Kimura 2-parameter methods  (Kimura 1980) , using the MEGA X software  (Kumar et al. 2018). Overall similarity among the genomes was evaluated using digital DNA-DNA hybridisation (dDDH) with the Genome-to-Genome Distance Calculator (GGDC) 2. 1 web server (http:// ggdc. dsmz.
Strain identification and phylogenetic analysis Strain Marseille-Q7820 was isolated from an endometrial biopsy. After performing a comprehensive analysis using MALDI-TOF mass spectrometry, we failed to identify the isolate. The score obtained was less than 1. 8, indicating that the species was not in the database and could potentially belong to an unknown species. The 16S rRNA sequence of the Marseille-Q7820 strain (OX458243. 1) revealed 98. 85% similarity to Cellulomonas hominis strain CE40 (NR_029288. 1) and 98. 45% to Cellulomonas pakistanensis strain NCCP-11 (NR_125452. 1), the closest phylogenetically related species with standing in nomenclature. The phylogenetic tree in Fig. 1  presents the position of strain Marseillle-Q7820 in relation to other closely related species with a validly published name.