dataesr/scientific-paragraphs-categorization huggingface dataset:
The additional forward primer P2 was used when appropriate for sequencing the longest 5 ETS variants. Sequence data analysis was conducted using Geneious R11 software (https://www. geneious. com, accessed on 12 July 2023), and Multiple Sequence Comparison by Log-Expectation (MUSCLE) multiple alignments were performed using either Geneious or Seaview5  [67]. The QGIS software 3. 10 (https://www. qgis. org/, accessed on 12 July 2023), including the base map SRTM, © Jet Propulsion Laboratory 2010, was used to georeference the At66 populations and to display associated pie charts illustrating the proportion of each 3 ETS or 5 ETS genotypes.
Acknowledgments: We thank  Martin Lysak  and  Terezie Mandáková  for supplying us with Brassicaceae leaves ( Central European Institute of Technology, Masaryk University, 62500 Brno, Czech Republic ),  Craig Pikaard  ( Department of Biology and Department of Molecular and Cellular Biochemistry, Howard Hughes Medical Institute, Indiana University, Bloomington, Indiana 47405 ) and  Julie Jacquemin  ( Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany ) for supplying us with seeds of Arabidopsis (either lyrata and halleri or cebennensis and pedemontana). We thank  Marie-Christine Carpentier  and  Martin Rosalie  who give courses and provide training workshops in R language and environment for statistical computing and graphics at UPVD and LGDP.
Funding: This work was funded by the  Centre National de la Recherche Scientifique (CNRS) , the  Université de Perpignan Via Domitia (UPVD) , the " Laboratoires d'Excellence (LABEX) "  TULIP  ( ANR-10-LABX-41 ), through  Labex AGRO  (under  I-Site Muse framework ) coordinated by the  Agropolis Foundation  (grant no. Flagship Project  1802-002-CalClim ) and by the  ANR  grants  MetRibo 20-CE12-0024  and  RiboStress 17-CE12-0026. This study was initiated with the support of a small " Bonus Quality Recherche " project from  UPVD  and set within the framework of the " Laboratoires d'Excellence (LABEX) "  TULIP  ( ANR-10-LABX-41 ) and the  École Universitaire de Recherche TULIP-GS  ( ANR-18-EURE-0019 ).
Results and discussion An anomalously dense open ocean phytoplankton bloom On 11-12 March 2019, during the Southern Ocean Ecosystem 2019 cruise, the RV Kronprins Haakon crossed a substantial phytoplankton bloom spanning 9000 km 2 (300 km long, 30 km wide and aligned along the 3500-4000 m isobath), in the Kong Håkon VII Hav, eastern Weddell Gyre (Fig. 1a ). We detected this bloom in an open ocean region with water depths over 3000 m, via analyses of ocean color data derived from ESA and EUMETSAT Sentinel-3A/B satellite images (see Methods). A Seaglider was deployed at the northern edge of the bloom (see Methods for a description of the glider trajectory through the bloom) and observed that the phytoplankton (i. e. , chlorophyll a (Chl a)) and Particulate Organic Carbon (POC), as retrieved from a Chl a fluorescence and a backscattering sensor mounted on the Seaglider and calibrated with in situ Chl a and POC measurements (see Methods), were distributed from the surface to 50 m depth (to approximately the base of the surface Mixed Layer (ML)).
All samples were blank corrected using carefully prepared 0. 2 μm filtrates  59. For δ 18 O analyses, samples were collected in 25 mL Wheaton bottles. The caps were sealed with Parafilm® and the bottles were stored in +4 °C and dark conditions until analysis in the laboratory. The analysis of δ 18 O was done with a Thermo Fisher Scientific Delta V Advantage mass spectrometer with Gasbench II. The δ 18 O data were standardized to Vienna Standard Mean Ocean Water (VSMOW). The reproducibility of the replicate analyses was ±0. 04‰. For the analyses of Dissolved labile (dFe) and total acid leachable (TaLFe) iron, water samples were collected at 12 stations with 5 L acid-cleaned Teflon-lined GO-FLO bottles deployed on a 500 m aramid rope (6 mm-vpg industri), using a dedicated winch and Teflon coated messengers.
Seaglider deployment and data treatment An autonomous Seaglider (SG563) was deployed and recovered during the cruise, collecting vertical profiles during 31 dives along a 55 km transect across the bloom region between March 12 and March 15, 2019 (Fig. 1a ). The glider was remotely operated by Norgliders (http:// norgliders. gfi. uib. no/), profiling continuously between the surface and 1000 m depth and measuring a suite of parameters that includes conductivity (salinity), temperature, depth (CTD), dissolved oxygen, Chl a, and optical backscattering by particles as a proxy for POC. The Seaglider Chl a fluorescence and backscattering signals were calibrated against the Chl a and POC concentrations measured during a calibration CTD cast shortly after the Seaglider deployment (2. 5 h). The Seaglider Chl a fluorescence and backscattering signals were processed following the glider toolbox described by Gregor et al.
Satellite data (Chl a, sea ice and winds) The Chl a concentration maps are extracted from the Ocean and Land Colour Instrument (OLCI) Level-2 full resolution Near Real Time (NRT) product, obtained by the EUMETCast broadcast system. The OLCI sensors on board ESA Sentinel-3A and B satellites launched in February 2016 respectively April 2018, and have large swath widths (~1270 km) covering large regions with high temporal resolution (approximately 1. 5-day global coverage with both sensors). The OC4ME algorithm which uses a polynomial approach of a maximum band ratio algorithm of 4 reflectances at 443, 490 and 510 nm over the 560 nm was extracted directly from OLCI Level 2 products and gives the Chl a pigment concentration in [mg/m3]. The cloud free chlorophyll orbit tiles are binned and averaged on a daily base to obtain one image per day.
This leads to a very good resolution, coverage in reasonable timeframe and meets the requirements of monitoring water dynamics also on smaller spatial scales. The analysis of satellite-derived ocean color was complemented using SeaWiFs (1997-2002, https://oceandata. sci. gsfc. nasa. gov/ SeaWiFS/) and MODIS (2003-2020, https://oceandata. sci. gsfc. nasa. gov/MODIS-Aqua/) level 3 Chl a data processed with the default chlorophyll algorithm (chlor_a) which employs the standard OC3/OC4 (OCx) band ratio algorithm merged with the color index (CI) in 8-day and 9 km resolution. First, the long-term analysis of the bloom presence (Fig. 2a ) was performed from January 1997 to December 2020, covering the oceancolor satellite era. We defined the bloom presence when the 8-day mean satellite-derived Chl a, averaged over 4°-8°E and 67.
8°-68. 4°S, was larger than the 23-year long mean Chl a + 1 standard deviation over that area (i. e. , 1. 14 mg m -3 , Fig. 2a ). In addition, the bloom duration in 2019 was calculated as the period between the first occurrence of the bloom during the Austral summer 2019 and its end, before the following Austral winter. The long-term median of the average Chl a concentration over the area 4°-8°E and 67. 8°-68. 4°S was used as a threshold to detect the bloom onset and end above which we considered a phytoplankton bloom present. Because of the presence of clouds and sea ice, satellitederived ocean color did not detect any pixel with Chl-a data in the bloom area before January 9, 2019, and after March 14, 2019, which we, thus, defined as the bloom duration. It is possible, however, that the bloom started earlier and terminated later than our conservative estimate.
Daily sea ice concentration (1997-2020) were obtained from the NASA's Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and Defense Meteorological Satellite Program (DMSP)-F13, -F17, and -F18 Special Sensor Microwave/Imager (SSM/I). Data with a spatial resolution of 25 km were provided by the National Snow and Ice Data Centre, University of Colorado in Boulder, CO (http://nsidc. org), with prior processing using the NASA team algorithms  62. Daily east-west and north-south wind components at the sea surface were obtained for the study region from the ERA-Interim global atmospheric reanalysis  63.
Calculation of net primary productivity and iron demands To calculate Net Primary Productivity (NPP) in the area where we detected the bloom, we used a 2. 25-degree bounding box around the position of the station where we deployed the glider and calculated the mean monthly NPP from satellite data products (VGPM, Eppley-VGM, Platt and CbPM). We further converted NPP from all methods into Fe demand (μmol m -2 d -1 ) using the mean ratio of Fe:C (4. 3 μmol:mol) generated for the Sub-Antarctic Zone of the Southern Atlantic Ocean  64.
Helium data from GLODAP The GLODAPv2. 2019 dataset  65  was used to retrieve helium data from our region of interest in the Atlantic sector of the Southern Ocean. The GLODAP Version 2 data product was obtained from https://www. glodap. info/. Helium data from a recent expedition (Transektokt 2020/21) Onboard the cargo vessel Malik Arctica in December 2020 to January 2021, seawater was collected from a SBE 32 carrousel water sampler connected to a CTD (conductivity-temperature-depth) SBE911+ system. For helium analyses, water samples were collected into copper tubes, which were clamped off after sampling and later analyzed at the IUP Bremen noble gas mass spectrometry lab HELIS. Samples were processed initially with an ultra-high vacuum gas extraction system. For analysis of the noble gas isotopes [3He, 4He] the extracted gas is transferred into a fully automated mass spectrometric system equipped with a two-stage cryogenic trap system.
Tracking of Antarctic petrels Breeding adults were captured on theirs nest during late incubation and mid chick rearing (from early January to mid-February) and instrumented with Global Positioning System (GPS) loggers (CatTrack 1, Catnip Technologies Ltd, Anderson, USA) just before leaving on a foraging trip. The GPS units weighed approx. 20 g (ca. 3% of bird body mass) and were taped to tail feathers. Birds were recaptured upon return to their nest to retrieve the GPS units and download the data. GPSs recorded the locations of the birds along their foraging trip at 5 to 30 min intervals. In total, data were available from 161 tracks collected in January/February 2012, 2013, 2014, 2016 and 2018. Additional details about the procedure are given in  51, 52. The utilization distribution maps (or heat maps) were created with the kernelUD function of package adehabitatHR in R software version 4.
Acknowledgements The research cruise to the eastern Weddell Gyre onboard the RV  Kronprins Haakon  was funded by the  Norwegian Polar Institute , with additional support from the  Norwegian Ministry of Foreign Affairs. This study was also funded by the  Research Council of Norway (RCN)  (grant number  288370 ) and the  National Research Foundation ,  South Africa  (Grant  UID 118715 ) through the  Norway-South Africa collaborative project  " Southern Ocean phytoplankton community  characteristics, primary production, CO 2 flux and the effects of climate change ( SOPHY-CO2 )" within the SANOCEAN framework (A. F. , S. J. T. , S. M. , T. R. -K. , L. d. S. , M. C. , P. M. , M. A. ). This study was also inspired by the  Research Council of Norway (RCN)  funded project " I-CRYME: Impact of CRYosphere Melting on Southern Ocean Ecosystems and biogeochemical cycles " (grant number  335512 , S.
Data availability The Seaglider, the biogeochemical and the taxonomical data from the scientific campaign can be found at the Norwegian Polar Data Centre (Norwegian Polar Institute): https://data. npolar. no/dataset/ ab96f43d-b813-4457-a466-6982c2c60a6b, https://data. npolar. no/ dataset/28fbddd2-0fb2-41c9-9f42-60146e28617f and https:// data. npolar. no/dataset/283e500c-732b-4f9b-a48a-3bc4990e3f55. In addition, the marine birds and mammals distribution data can be found at https://data. npolar. no/dataset/5168ad7f-4733-45fd-87bd-4c4c4c217876. Atmospheric reanalyses can be obtained from https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/ era-interim. Satellite derived ocean color is available at https:// oceandata. sci. gsfc. nasa. gov/ and https://www. eumetsat. int/oceancolour-services.
C. CFD database: Parametrization Were our computational means truly unbounded, we would run one computation for each set of the parameters and each micro-environment, and construct a concentration map for each of these. This is not computationally feasible in the real world. To circumvent this problem, we build a finite database of CFD simulations and concentration maps in a limited number of situations, from which the required maps will be derived through a suitable change of variables (under certain approximations) or interpolated. Let (x,y) be the earthbound frame centered on the emit- ter E at time t e and let v m be E's vectorial velocity (walking) at that time t e and v w be the wind velocity; we denote e m and e w the corresponding unit vectors (directions). Note that the head direction e h does not necessarily align with e m in practice, for instance during a conversation, as illustrated in Fig.
This choice entails that the co-moving frame is rotated by an angle θ h = ( e x , e h ) and translated at a constant speed v m , with respect to the lab frame. In the co-moving frame, sketched in Fig. 2(c ), the wind blows with a velocity v w = ( v w -v m ); it is modeled by imposing a uniform velocity field v w , parallel to the ground, as a boundary condition in the far field. Note that v w may result from the emitter's motion, the wind, or from both of them. Finally, we denote ϕ the angle between -v w and the direction of emission e x. Thus, v = || v w || and ϕ fully parameterize the CFD database. For instance, in this database, the above situations in which an emitter walks at 1 m • s -1 in still air and a static emitter faces a headwind blowing at 1 m • s -1 both correspond to ϕ = 0 and v = 1 m • s -1.
D. CFD database: simulation details The CFD simulation protocol is detailed in Appendix B. In short, a still-standing manikin mimics a man who is breathing through the mouth, at a rate of 20 breaths of 1 L per minute, i. e. , one breath every 3 seconds, with an equal time for exhalation and inhalation. This signal was originally designed to replicate the breathing flow rate of a walking person, but also applies for speaking. Largeeddy simulations are performed along the same lines as  [21, 30] , using the incompressible version of the Navier-Stokes equations, which was found to provide the best compromise between cost and accuracy of the simulations. Each simulation starts with 3 cycles to establish the flow, followed by 4 cycles during which statistics are collected. Echoing Abkarian et al.
[30] , we remark that the unsteady starting jets close to the mouth tend to form a main jet whose characteristics far from the mouth resemble those of a steady jet, with a limited influence of the details of the exhalation signal. This is why we use the same aerodynamic simulations to model mouth breathing and speaking. However, the number and sizes of emitted droplets will differ between breathing and speaking. Overall, the database consists of 25 microscopic simulations for ambient relative velocity v (m • s -1 ) ∈ S v ={0, 0. 1 0. 3, 1. 0, 2. 0 } and ϕ ∈ S ϕ ={0, π 6 , π 3 , π 2 , 3π 4 , π}, plus the case v = 0. 0 m • s -1 , for which ϕ is undetermined (the simulations at v = 0. 1 m • s -1 were used for control exclusively). The simulation cost increases with v. For v = 2. 0 m • s -1 , a simulation takes more than ≈ 80 hours on 5 AMD EPYC Rome 7H12 bi-sockets nodes (640 cores) of the IRENE-AMD partition of Joliot-Curie cluster (TGCC/CEA, France).
D. Interpolation of spatiotemporal diagrams Given that the CFD database only contains a limited number of cases (v, ϕ) ∈ S v × S ϕ , interpolation is needed to obtain the spatiotemporal diagram corresponding to given relative wind conditions (v, ϕ) at a delay τ. We select the two closest values ϕ 1 and ϕ 2 (ϕ 1 ϕ ϕ 2 ) in S ϕ and v 1 and v 2 (v 1 v < v 2 ) in S v. Now, since ϕ controls the rotation of the exhaled air puff under the wind and v affects the propagation dynamics, a naïve linear interpolation over ϕ and v would perform poorly. Instead, noticing from Fig. 4  and its kin that the puff is first transported in the direction of emission, and then in the wind direction, we handle (very) short delays τ τ (v) =0. 12/v distinctly from longer delays τ > τ (v). For the former, the directions of emission are already aligned with e x in the spatiotemporal diagrams, so no rotation is needed.
A. Empirical crowd dynamics We begin with a presentation of the empirical scenarios that will be used as test-cases in our macroscopic risk assessments. They are listed in Tab. I and include fairly busy streets (and riverbanks), metro and train stations, an outdoor market, and street cafés. All scenarios are in outdoor settings or in large, well ventilated areas and the data were collected in the metropolitan area of Lyon, France, during the COVID-19 pandemic, between July 2020 and January 2021. Note that these data are openly available on the Zenodo platform [84]. More details about the data acquisition and pedestrian tracking protocols can be found in Garcia et al. [21]. In short, the pedestrians' positions and head orientations are marked on the videos every 0. 5 s; the temporal resolution is then increased to a point every 0.
E. A practical guide to assessing risks in real or test scenarios We close this section with a succinct summary of the practical way in which risks of viral transmission can be assessed using our framework: • Collect or simulate the (x, y)-trajectories of people in the scenario; the head orientations can be measured or assumed to align with the walking direction; • If needed, interpolate between points in order to increase the temporal resolution; • Feed these trajectories into the Python scripts coupling them to the dynamic concentration maps to get the mean rates of new infections C(∆T ) for each expiratory activity and wind velocity under study • Estimate the fraction of time α t spent talking and the fraction of time α b spent breathing through the mouth; • Compute the rate of new infections as a weighted average [86] of the C(∆T ) α over the expiratory activities α (if talking is not negligible, then C(∆T ) C(αt ∆T ) talking ) at the typical external wind speed v w , or averaged over the distribution of different wind speeds and wind directions.
Our study further shows how the walking velocity of pedestrians also contributes to decreasing the risks, through the same mechanism. By coupling these binary transmission risks with pedes- trian trajectories, the rates of new infections generated by a contagious individual wandering for one hour on the premises was assessed. We put this to the test using field data collected in diverse daily-life scenarios, which were thus ranked by risks. Consistently with our previous findings with much coarser models, street cafés present the highest risks among the investigated scenarios in windless conditions, due to the configuration of the crowd (even if the larger fraction of time spent talking is left aside), followed by the observed outdoor market and, further down the list, train and metro stations (at the peak of the pandemic).
Along similar lines, the influence of the receiver's exhalation and inhalation flow on the transport of droplets generated by an emitter  [48]  could be taken into account, even though this may require many more CFD simulations. Height differences between people could also be incorporated, and should be so if the crowd mixes standing and seated people. To allow other researchers to contribute to these prospective improvements and/or apply the proposed approach, our main scripts have been made publicly available on the GitHub repository https://github. com/an363/InfectiousRisksAcrossScales; the other scripts can be requested by email.
Boundary conditions The CFD database is parametrized by the incident air speed v and the angle ϕ between minus the incident velocity vector and the mouth direction (Fig. 2 ). The inflow boundary condition mimics an ambient wind, where a uniform flow of v w = (-v cos ϕ, v sin ϕ, 0) is imposed; the outflow boundary condition is applied on the other side of the domain, and slipping wall boundary conditions are applied to the lateral boundaries. The breathing flow is injected at the manikin's mouth, which was delimited by hand as the surface covered by the lips of the manikin, whose mouth is initially closed. This yields a non-planar surface of 4. 7 cm 2 on which a uniform velocity is imposed, parallel to the ground and in front of the manikin. The time signal is periodic, with a period of 3. 0 s, and was designed to mimic a breathing signal  [81]  with a short period typical of the breathing pace while walking.
ACKNOWLEDGMENTS The setup of the CFD simulations was designed collectively, with  P. Bénard ,  G. Lartigue ,  V. Moureau  ( CORIA Rouen, France ),  G. Balarac ,  P. Bégou  ( LEGI Grenoble, France ),  Y. Dubief  ( Univ. Vermont, USA)  and  R. Mercier  ( Safran Tech, France ). We thank our colleague  O. Kaplan  for generating the smoke jets of Fig. 3. CFD simulations were performed using HPC resources from TGCC-IRENE (Grants No. AP010312425  and  A0100312498 ). AN acknowledges the help of B. Fray during his internship and thanks the  MODCOV group  for facilitating the initiation of collaborations. This work was funded by  Agence Nationale de la Recherche : projects  SeparationsPietons  ( ANR-20-COV1-0003 ,  A. Nicolas ) and  TransporTable  ( ANR-21-CO15-0002 ,  S. Mendez ).
The study of extreme weather events and their impact on ocean physics and biogeochemistry is challenging due to the difficulty involved with collecting in situ data. However, recent research has pointed out the major influence of such physical forcing events on microbiological organisms. Moreover, the occurrence of such intense events may increase in the future in the context of global change. In May 2019, an intense storm occurred in the Ligurian Sea (north-western Mediterranean Sea) and was captured during the FUMSECK (Facilities for Updating the Mediterranean Submesoscale -Ecosystem Coupling Knowledge) cruise. In situ multi-platform (vessel-mounted acoustic Doppler current profiler, thermosalinometer, fluorometer, flow cytometer, a moving vessel profiler equipped with a multi-sensor towed vehicle, and a glider) measurements along with satellite data and a 3D atmospheric model were used to characterise the fine-scale dynamics occurring in the impacted oceanic zone.
, 2019). Published by Copernicus Publications on behalf of the European Geosciences Union. Meteorological impulse wind events, such as storms, and their effects on oceanic physics and biogeochemistry are poorly explored with in situ data. Such events generate mixing and stirring of the surface layer and can trigger transitional peaks in primary production, mainly explained by nitracline shoaling and grazer dilution  (Lomas et al. , 2009; Menkes et al. , 2016). Under oligotrophic ocean conditions,  Babin et al. (2004)  and  Han et al. (2012)  observed sudden and large increases in chlorophyll a (Chl a) from satellite ocean colour data that lasted for several weeks after summer hurricane storms. The resulting increase in Chl a integrated over the first few metres reached values close to those of the spring bloom  (Babin et al.
Transect measurements The vessel-mounted acoustic Doppler current profiler (VM-ADCP; RDI Ocean Surveyor 75 kHz) continuously acquired data during the cruise. The vertical range was set to [18 m, 562 m] with an 8 m resolution. Current data were averaged and stored every 2 min, corresponding to a horizontal resolution of 0. 4 km for a vessel speed of 6. 3 kn. The resulting horizontal currents have been processed with Cascade 7. 2 software (Le  Bot et al. , 2011). The surface-water flow-through system pumped seawater at a 2 m depth with a flow rate of about 60 L min -1. A thermosalinograph (TSG, Sea-Bird SBE 21) acquired sea surface temperature (conservative, _tsg) and salinity (absolute, S_tsg) data every minute. A fluorometer (Turner Designs, 10-AU-005-CE) simultaneously recorded sea sur-face red fluorescence > 680 nm after excitation in the blue (Rfluo_tsg (a.