dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary A diverse array of phytoplanktonic species supports the ocean's biogeochemical cycles, occupying and thriving in a wide range of environments within the sunlit regions of the ocean. One such environment is found at the base of the euphotic zone and oxycline, within the OMZ, where a unique community of phytoplankton is known to thrive. Monitoring this deep community of phytoplankton, however, is challenging, and as a consequence, little is known about its dynamics. Here, the two-community model of  Brewin et al. (2022)  was extended to include a third community (C 3 ) that thrives in a low-light and low-oxygen environment, within the OMZ. The model was tuned to data collected by a BGC-Argo float in the ETNP. Analysis of the model and its parameters revealed the model to reproduce effectively the patterns of both Chl a concentrations and particulate backscattering throughout the water column.
Acknowledgments We acknowledge all those involved in the deployment and of the BGC-Argo float (WMO number  3901531 ) used in the study. This work was supported by a  UKRI Future Leader Fellowship  ( MR/V022792/1 ); the  European Space Agency (ESA)  project  Biological Pump and Carbon Exchange Processes (BICEP ); by the  Simons Foundation   Project Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES ;  549947 , SS); and by the  Royal Society , in the framework of the  Newton International Fellowship  (grant  NF150203 ). Isabelle Cox  was supported by a  MSc in Marine Vertebrate Ecology and Conservation , in the  College of Life and Environmental Sciences , and a PhD studentship in the  Centre for Geography and Environmental Science , both at the  University of Exeter.
Data availability statement These data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo. ucsd. edu, https://www. oceanops. org). The Argo Program is part of the Global Ocean Observing System. All data and code used in the article are provided openly on a GitHub page (https://github. com/ rjbrewin/Three-community-phyto-model). This includes an example Jupyter Notebook Python Script, processing this BGC-Argo float and tuning the models.
Context. The study of the early phases of star and planet formation is important to understand the physical and chemical history of stellar systems such as our own. In particular, protostars born in rich clusters are prototypes of the young Solar System. Aims. In the framework of the Seeds Of Life In Space (SOLIS) large observational project, the aim of the present work is to investigate the origin of the previously inferred high flux of energetic particles in the protocluster FIR4 of the Orion Molecular Cloud 2 (OMC-2), which appears asymmetric within the protocluster itself. Methods. Interferometric observations carried out with the IRAM NOEMA interferometer were used to map the silicon monoxide (SiO) emission around the FIR4 protocluster. Complementary archival data from the ALMA interferometer were also employed to help constrain excitation conditions.
To complement our analysis, we used SiO J = 5-4 archival data from the ALMA telescope (project 2017. 1. 01353. S, PI: S. Takahashi), which include observations with both the main (12-m antennas) and the compact (7-m antennas) array. A full detailed analysis of the ALMA observations was described recently in  Sato et al. (2023). The observational setup is presented in the following section. In Sect. 3, a description of the analysis and the obtained results is provided. The model for the particle acceleration is also presented therein before the discussion (Sect. 4). The main outcomes of the work are described in the last section.
NOEMA The interferometric observations were carried out in several runs in 2016 and 2017. The IRAM NOEMA array was used in C configuration as part of the SOLIS large programme  (Ceccarelli et al. 2017). All data reported here were obtained with the WideX band correlator, which provides 1843 channels over 3. 6 GHz of bandwidth with a channel width of 1. 95 MHz (∼6. 5 km s - 1  at 86 GHz). The phase centre of the observations was RA(J2000) = 05 h 35 m 26. s 97, Dec(J2000) = -05 • 09 ′ 56. ′′ 8, and the local standard of rest velocity was set to 11. 4 km s -1 , and the systemic velocity was OMC-2 FIR4  (Shimajiri et al. 2015; Favre et al. 2018). The primary beam is ∼54 ′′ at 86 GHz, and the system temperature during the observations ranged from 60-100 K (∼200 K in summer) with an amount of precipitable water of ≲5 mm (10-15 mm in summer).
The maximum recoverable scale is 20 arcsec. The absolute flux scale was calibrated by observing the quasars LKHA101 and MWC349, while 3C454. 3 and 3C84 were used as a calibrator for the bandpass shape. For gain (phase and amplitude) calibration, 0414-189, 2200+420, 0524+034, and 0539-057 were used. Calibration and imaging were performed using the CLIC and MAPPING software of the GILDAS package 1 , respectively. The continuum was imaged by averaging the line-free channels of the WideX backend. The continuum image was self-calibrated and the solutions were applied to the spectral lines. A natural weight was used in the visibilities, and all the cleaning of the detected spectral features was performed using the Högbom method  (Högbom 1974). All the maps presented in this work are primary beam corrected, and the final synthesised beam is 3.
ALMA Additional SiO emission data from OMC-2 FIR4 were obtained from Atacama Large Millimeter/submillimeter Array (ALMA) observations in the frame of the 2017. 1. 01353. S project (PI: S. Takahashi), with an observing run in April 2018 for a total time on source of ∼ 30 min. A total of 44 antennas were used for the observations in Band 6 (1. 3 mm); flux and bandpass calibration were obtained through observations of J0522-3627, while the quasar J0541-0541 was used for phase and amplitude gain calibration. The shortest and longest projected baselines are 15 and 500 m, respectively, with a maximum recoverable scale of ∼11 arcsec. The data were processed and primary beam corrected using standard ALMA calibration scripts of the Common Astronomy Software Applications (CASA 2  , version 5. 4. 0) package.
The spectra are shown in Fig. 2  and were extracted from the NOEMA and ALMA maps from three regions 3  : the bright spot (6σ level) map close to the HOPS-108 protostar and two regions encompassing emission knots chosen along the jet (yellow ellipses in Fig. 1 ). The region close to the protostar was labelled R1 (in Figs. 1  and 2 ) and the farthest R3, with the R2 being halfway between those two. For consistency, the ALMA maps around the SiO 5-4 emission line, exhibiting the same morphology of the SiO 2-1 emission, were convolved  with a 2D Gaussian beam to match NOEMA angular resolution and regridded to the same pixel size. The velocity resolution of the ALMA data was also binned to match the NOEMA one (∼7 km s -1 ). Despite the low velocity resolution, it is clear from Fig. 2  that the R1 emission is at a higher velocity with respect to the systemic velocity of the envelope (11.
With all the limitations due to the current dataset, the model indeed derives physical-chemical parameters in accordance with those obtained by previous works. In particular, the H 2 volume density is in good agreement with the values obtained using different techniques (e. g. Ceccarelli et al. 2014; López-Sepulcre et al. 2013). Similar considerations apply to the results of the non-LTE analysis of the SiO emission spectra extracted from the regions along the jet itself. The values of the kinetic temperature and density obtained by modelling the ratio of the observed transitions are consistent with those obtained by previous studies (20 K < T kin < 50 K for densities ∼5 × 10 6 cm -3 ).
Acknowledgements. This project has received funding within the  European Union 's  Horizon 2020 research and innovation programme  from the  European Research Council (ERC)  for the project "The  Dawn of Organic   Chemistry" (DOC) , grant agreement No  741002 , and from the  Marie Sklodowska-Curie  for the project " Astro-Chemical Origins " (ACO) , grant agreement No  811312. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2017. 1. 01353. S. ALMA is a partnership of ESO (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) , MOST and  ASIAA  (Tai-wan), and  KASI ( Republic of Korea) , in cooperation with the  Republic of Chile. The  Joint ALMA Observatory  is operated by  ESO ,  AUI/NRAO  and NAOJ. V. L. , F. O. A. , and P. C. acknowledge financial support from the  Max Planck Society.
The negative lobes in the ALMA spectra, especially in R1 and R3, are likely due to the filtering of larger scale SiO emission of the ALMA interferometer. These effects are present both the in the 12 m array and in the compact array data, and both configurations have a maximum recovery scale smaller than the NOEMA array, whose data do not show this effect. If somehow this effect impacts the line width of the derived spectra, and in part its absolute brightness peak, this information is not the focus of the present work; only the latter quantities were used, with substantial uncertainties, in the non-LTE analysis. The purpose of the displayed spectra here is to show the kinematics of the SiO peaks at the different locations, i. e. the three regions along the jet emission.
Apatite U-Pb and Trace Element Analyses. Apatite U-Pb isotopes and trace elements in individual apatite grains were acquired simultaneously from the same ablation using an Agilent 8900 ICP-QQQ coupled to an ESI New Wave NWR 193 UC (TwoVol2) laser ablation system at the Beijing Quick-Thermo Science and Technology Co. , Ltd. Ablation was carried out for 25 seconds with a 40 µm diameter beam size, c. 3 J/cm 2 energy and 5 Hz repetition rate on the selected grains and reference materials, following Reference 27. Data obtained by LA-QQQ-ICPMS were processed using the Iolite data reduction software  [28]. For trace element analysis, 43 Ca was used as the internal elemental standard. The primary trace-element reference material employed was NIST SRM 612 standard glass, with Durango apatite used as a secondary trace-element reference material.
Based on the pronounced but not total annealing of these samples, the single-grain age pattern suggests exposure to temperatures of c. 95°C-125°C (paleodepths of c. 4-5 km) at c. 30 Ma, sufficient to nearly completely reset the single-grain ages at that time (younger peak ages in Figure  9 (a)), such that ages of the youngest component grains can be explained as the onset of significant cooling and exhumation at c. 30 Ma (Figure  9 ). Thermal history modeling using the software HeFTy (ver. 1. 9. 1  [43] ) was performed on three deeper samples BK2-4 (Figure  10 ). We use the fact that these samples were at the surface at the age of deposition as the principal constraint in the models, together with a temperaturetime box around the AFT ages with temperature intervals ranging from 60°C to 140 °C, a little broader than the typical APAZ.
Methodology The study methodology consists of the following phases: (1) A compilation of extreme weather events occurring on the coast during the period from 1 January 2009 to 28 February 2020. A preliminary database is created with the following fields: • Identification of the event: event number, country, region, local name, starting date, final date, and coordinates of the primary focus. • Characterization of the event: processes that took place (flooding, landslide, rockfall, beach retreat, coastal erosion, etc. ), triggering factors, fatalities, injures, damage to, economic losses, emergency plan activated, actors involved, adopted measures and repairing costs.
• Source of information: official sources, technical reports, scientific articles, media, links of interest and photos attachment. (2) Selection of the main extreme weather events using the following three criteria: • A selection of different types of events was made by considering meteorologically significant events with a regional impact: Atypical Cyclonic Storm (TCA), High Level Isolated Depression (HLID), significant squalls (with their own name) and intense precipitation episodes (>50 mm/24 h) with a regional extension. Extreme sea level events are also included. The main sources of data are the repository of climatological summaries of the meteorological agencies of each country: Météo France (France); Instituto Português do Mar e da Atmosfera (Portugal) and Agencia Estatal de Meteorología (Spain).
With this information, the database was completed by including relevant information for each event: duration and geographical extent, precipitation, temperature and precipitation anomalies, insolation and wind speed data. Some data concerning the maximum recorded wave height have been obtained from other national agencies related to port management. It should be noted that some events were supra-regional and international in scope, like the Gloria storm in January 2020, that hit many regions in the east of the Iberian Peninsula, as well as the Southeast coast of France.
• A selection of events with economic damage of more than €0. 5 M. For this purpose, official data from the Consorcio de Compensación de Seguros (Insurance Compensation Consortium of Spain) and official information from other institutions in France and Portugal have been used. Additionally, the information in the three sections has been supplemented with other databases, scientific articles and technical reports. In the case of information gaps, contrasted data from the press and other media were consulted, to validate the estimates with the criterion that the same item of data appears as it is more than once. Once the three main data sources (extreme weather events, economic damage and fatalities/injuries) were organised, they were overlaid to select the events with the greatest impact. Figure  2  shows the methodological phases used in the present work.
However, the AIS also changes on much longer timescales than those covered by the direct observations (e. g. ,  Colleoni et al. , 2018) , and the long-term records of the SMB are needed to better understand the AIS mass changes  (Stokes et al. , 2022). In this study, we use the term accumulation or accumulation rate to refer to positive SMB because the long-term SMB is generally positive on the interior part of AIS. Firn and ice cores have provided the AIS SMB histories over the last few centuries to millennia. Thomas et al. (2017)  compiled 79 records of Antarctic ice-core-based SMB records over the past 1000 years. The analysis of the total AIS SMB derived from the database suggested that snow accumulation had significantly increased in the 20th century (over the past ∼ 50 and ∼ 100 years)  (Thomas et al.
, 2017). A newer SMB reconstruction over the last 200 years by combining the above dataset and reanalysis data suggests accelerating the increase in AIS accumulation rate during the latter half of the 20th century  (Medley and Thomas, 2019). In the East Antarctic Plateau (EAP), the ice-core-based SMB reconstructions do not show a uniform trend. At the Kohnen station (EPICA DML), the increasing trend of accumulation rate was reported over the past 200 years  (Oerter et al. , 2000; Hofstede et al. , 2004; Altnau et al. , 2015). Along the Japanese-Swedish traverse route between the Wasa and Dome Fuji stations,  Fujita et al. (2011)  combined shallow cores and ice radar isochrons and found a strong relationship between the SMB and local surface topography and a higher accumulation rate in the latter half of the 20th century than the preindustrial Holocene average by 15 %.
, 2008) ; thus, an SMB reconstruction from one core may have large uncertainty. (3) Density profiles in the low-accumulation area are highly variable, especially near the surface  (Weinhart et al. , 2020) , introducing large uncertainty into the estimated masses. (4) Sparseness of volcanic eruption records before 1000 CE and high-resolution ice core datasets, combined with occasional lack of annual layers in the low-accumulation cores, make it difficult to precisely and densely date the ice cores. Thus, the existing SMB reconstructions in EAP over more than several thousand years are based on a proxy method (from stable water isotopes; e. g. ,  Parrenin et al. , 2016)  or a small number of isochrons from shallow ice radar measurements (e. g. ,  Fujita et al. , 2011; Cavitte et al. , 2018 Cavitte et al.
, , 2022)). In this study, we reconstruct the SMB histories around Dome Fuji over the last 5000 years using data from seven snow pits and 15 shallow ice cores retrieved by successive Japanese Antarctic Research Expedition (JARE). We determined the depth-age relationships of the ice cores by synchronizing them with the WAIS Divide ice core, whose chronology is accurately determined by annual layer counting using common volcanic signals  (Sigl et al. , 2014 (Sigl et al. , , 2016)). We discuss millennial-scale trends and centennial-scale variabilities of SMB in relation to the climate variabilities during the middle to late Holocene.
At the Dome Fuji station, two deep ice cores have also been drilled in the 1990s and 2000s to the depths of 2503 m (340 ka BP) (DF1) and 3035 m (720 ka BP) (DF2), respectively  (Watanabe et al. , 1997a, b; Dome Fuji Ice Core Project Members, 2017; Motoyama et al. , 2021). Two shallow cores (DF1993 and DF2001) are the shallow parts of the DF1 and DF2 deep ice cores. Because the NDF2018 core is the longest and oldest shallow ice core in this study (151 m long, covering ∼ 4800 years), we use the records of DF1 and DF2 deep cores to cover the same period. Four ice cores (DF1, DF2, NDF2018 and NDFN cores) cover more than 4000 years, three ice cores (DF1997, DFS2010 and DFS2011 cores) cover 1500 years and six ice cores (MD364, NDF2013, S79, S80, DFNW and DFSE cores) cover 800 years or less. The DF1997 core has poor ice core recovery and uncertain depth assignments for 0-20 m; thus, we use the data only below 20 m.
Bulk density measurements We use the bulk density data for estimating the SMB for the MD364, S79, DFS2011, NDFN, DFSE and DFNW cores. Bulk density of an ice core piece was calculated from its diameter, length and mass measured at the drilling sites, assuming that it has a cylindrical shape. The diameter was measured with a caliper at one to three positions, and the mass was measured with an electronic balance. The density of near-surface snow was obtained by excavating a snow pit and sampling the snow along the pit wall with a stainless-steel snow density sampler, which was measured with an electronic balance. The typical dimensions of the density sampler are 60 mm (W ) × 56 mm (D) × 30 mm (H ) (∼ 100 cm 3 ). To assess the uncertainty of the in situ ice core measurements, the bulk density of the NDFN core was also measured between 21 and 64 m (correspond to ∼ 500-700 kg m -3 ) in a cold laboratory at the National Institute of Polar Research (NIPR).
The difference between the in situ and laboratory data likely originates in the harsh measurement environment in the field (wind, lack of time, uneven worktable, etc. ) and was 6 ± 15 kg m -3 (mean and standard deviation). We assume that the random error of bulk density measured in the field for the other cores is the same as that of the NDFN core (±15 kg m -3 ). For the Dome Fuji site, the density error can also be evaluated using the two cores (DF1993 and DF2001) separated by only 43 m. We resampled the density data at 0. available (191 intervals), and evaluated the variability of densities by calculating the pooled standard deviation (S p ) as the square root of the summed squared deviations of the DF1993 and DF2001 density data from the respective means for the 0. 5 m intervals, divided by the number of depth intervals: S p = 382 i=1 (ρ -ρ) 2 191 , (1) where ρ is density for the 382 individual data and ρ is the mean of DF1993 and DF2001 for the 191 depth intervals.
SMB histories from individual cores and snow pits To examine the spatial differences in accumulation rate in the inland plateau region around Dome Fuji, mean accumulation rates over fixed time intervals were calculated for each core. For the averaging periods, we chose (1) 1461-1816 CE (all cores), (2) 1-1850 CE (DF1, DF2, DFS2010, DFS2011, NDFN and NDF2018 cores), and (3) 2000 BCE-1 CE (DF1, DF2, NDFN and NDF2018 cores) (Fig. 7 ) for the following climatological reasons. 1. We chose 1461-1816 CE because it is the most accurately constrained period by the volcanic years common for all the studied ice cores and also a good representation of the preindustrial late Holocene for averaging SMB without decadal noise. 2. We chose 1-1850 CE because 1 CE is the start year of the "PAGES 2k network" datasets, which is connected to the Paleoclimate Modelling Intercomparison Project (PMIP) for providing the baseline information about natural climate variability (e.
In the detrended SMB record at Dome Fuji over the last 1000 years, the intervals of small SMB anomalies appear to correspond to those with large volcanic eruptions (e. g. , Samalas in 1257 CE, an unknown eruption in 1458 CE, Tambora in 1815 CE and Krakatau in 1883 CE), as well as the Spörer, Maunder and Dalton grand solar minima (Fig. 13 ). Note that the eruption in 1458 was previously estimated as being from Kuwae, but it is now interpreted as an unknown event (perhaps in the Southern Hemisphere)  (Hartman et al. , 2019). On the other hand, the Oort and Wolf grand solar minima do not correspond to low SMB anomaly periods at Dome Fuji, but they coincide with the low SMB anomaly periods in the EA composite. Thus, our data are partly consistent with the previous suggestions for the last 1000 years.
Conclusion We analyzed a total of 15 ice cores and seven snow pit samples to obtain 13 SMB records around Dome Fuji over the last 5000 years. Four ice cores cover more than 4000 years, three ice cores cover 1500 years, six ice cores cover 800 years or less, and the longest reconstruction covers 5152 years  (3151 BCE-2001 CE, DF2 core). Our new SMB reconstructions took advantage of detailed and precise depth-age controls for the ice cores owing to high-resolution volcanic synchronization with the WAIS Divide ice core. We also considered vertical ice thinning for all cores and used highresolution permittivity data for estimating high-precision density profiles for most of the cores. Because the accumulation rate is low and its variability is relatively large in the high-elevation plateau in East Antarctica (e.
Climate forcing We forced GRISLI with the absolute climatic fields from general circulation model (GCM) outputs of the PMIP3/PMIP4 database  (Kageyama et al. , 2021). All the GCMs for which LGM simulations were available at the time of writing the manuscript have been selected (see Table  1 ). Monthly surface air temperatures and solid monthly precipitation are used to compute the surface mass balance defined as the difference between snow/ice accumulation and ablation. Ablation is calculated using a positive degree-day (PDD) method following the formulation of  Tarasov and Peltier (2002). Snow accumulation is calculated from the total precipitation (rain and snow), considering only months where monthly temperatures are under the melting point. Due to the differences between GCM and GRISLI resolutions, the GCM outputs are bi-linearly interpolated onto the ice sheet model grid.
From this climate forcing ensemble, we only selected those leading to LGM ice sheets in a reasonable agreement with the most credible ice extent in the DATED-1 database  (Hughes et al. , 2016)  and with the geologically-constrained ice thickness reconstructions, namely ICE-6G_C  (Peltier et al. , 2015) , GLAC-1D  (Briggs et al. , 2014; Tarasov et al. , 2012; Tarasov and Peltier, 2002) , and ANU  (Lambeck et al. , 1995 (Lambeck et al. , , 1996 (Lambeck et al. , , 2010)).