dataesr/scientific-paragraphs-categorization huggingface dataset:
The entire earthquake dataset was first located using the aforementioned 1D initial velocity model (Figures 3a and S1 in Supporting Information S1). The phase quality for these events can be further assessed by plotting a Wadati diagram (Figure  3b ). This diagram is very useful to evaluate the phase association process as any point not fitting well with the linear relationship might correspond to phases incorrectly identified either by not being associated to the right event or by being of the wrong type. The central region of Chile is characterized by an important mining activity, with two of the world largest copper mines, Los Bronces and El Teniente, located within our study area (Figure  1 ). These sites, in particular, Los Bronces, which is of open-pit type, seem to produce an important seismicity.
Local Earthquake Tomography Once our quality control was completed, we selected earthquakes with a preliminary 1-D location obtained with 8 or more P-wave arrival times, 4 or more S arrival times, a focal depth of less than 150 km, and an azimuthal gap smaller than 200°. This resulted in a dataset of 5,899 earthquakes with 74,619 P arrival times and 57,635 S arrival times recorded at 77 seismic stations (Figure  3a ). About 90% of these phases were automatically detected by PhaseNet. The remaining 10% come from the CHASE dataset. All earthquakes and stations are contained within a rectangular region of 270  × 324 km in longitude and latitude, respectively, extending from 4 km above sea level to 152 km depth. All the rays are traced from the source to the receiver without the need for elevation corrections.
Conclusions In this study, we processed 4. 4 years (January 2017 to May 2021) of continuous waveform data recorded by the Chilean permanent seismic network, using a deep-learning approach. The resulting P and S travel time picks are combined and inverted with a dataset from a previous temporary seismic deployment, resulting in tomographic models of seismic velocities in the Chilean foreland crust with an unprecedented level of detail. The improved velocity models are used to compute 3-D probabilistic hypocenter locations resulting in a robust catalog of more than ∼14,000 events. Finally, double-difference relocations revealed structural details related to crustal shortening of the South-Central Andes. The tomographic models and improved hypocenter distribution are combined in order to reevaluate the conceptual model of crustal deformation along a key cross section at latitude 33.
Data Availability Statement Raw seismic waveforms used in this study are available from the IRIS platform (https://ds. iris. edu/ds/nodes/ dmc/). Network codes are C1, C, and G. Data preparation and preprocessing were performed using the ObsPy package  (Beyreuther et al. , 2010). Most of the figures were made with the Generic Mapping Tools (GMT) package  (Wessel & Smith, 2006)  and the Matplotlib library  (Hunter, 2007).
The South Vietnam Upwelling (SVU) develops in the South China Sea (SCS) under the influence of southwest monsoon winds. To study the role of small spatiotemporal scales on the SVU functioning and variability, a simulation was performed over 2009-2018 with a high-resolution configuration (1 km at the coast) of the SYMPHONIE model implemented over the western region of the SCS. Its capability to represent ocean dynamics and water masses from daily to interannual scales and from coastal to regional areas is quantitatively demonstrated by comparison with available satellite data and four in situ datasets. The SVU interannual variability is examined for the three development areas already known: the southern (SCU) and northern (NCU) coastal upwelling areas and the offshore upwelling area (OFU).
, 2019; Ngo and Hsin, 2021). Using long simulations or datasets, two recent studies established statistical relationships between the interannual variability of SCU and OFU intensity, the atmospheric forcing, and the ocean circulation. Da et al. (2019)  used a 1/12 • (∼ 9 km) 14-year simulation. Ngo and Hsin (2021)  used 1/4 • (∼ 28 km) datasets of 38-year SST and reanalysis winds and of 28-year satellite-altimeter-derived sea surface currents. Da et al. (2019)  simulated an Ekman pumping-related OFU that develops under the influence of strong positive wind curl. They obtained a highly significant correlation between the yearly intensity of upwelling and the summer average of wind stress (0. 70, p<0. 01) and wind stress curl (0. 72, p<0. 01) over the region. They showed that SVU intensity is also correlated with the summer average of positive vorticity of surface current over the offshore area (0.
Ngo and Hsin (2021)  confirmed the relationship between the interannual variability of the summer regional wind stress curl, the induced offshore circulation (i. e. , eastward jet, ACC dipole, and associated northward/southward boundary currents), and the SCU and OFU intensity: they showed that upwelling is stronger (weaker) for years of stronger (weaker) wind stress curl and large-Ocean Sci. ,  18, 2022  https://doi. org/10. 5194/os-18-1131-2022 scale circulation (eastward jet and dipole). Finally,  Chen et al. (2012)  showed that tides and river plumes could be additional mechanisms involved in SVU dynamics, using idealized simulations. Tide effect was not included in most modeling studies, including  Da et al. (2019). Most previous studies were therefore based on numerical or observational datasets associated with several methodological choices and hypothesis: short study periods and limited spatial coverage of in situ datasets; model resolution coarser than ∼ 10 km; cloud cover and proximity of the coast hindering satellite data quality; gridded current datasets built from along-track sea level satellite data that cannot capture small-scale and ageostrophic dynamics; synoptic view based on spatially and seasonally integrated atmospheric and oceanic variables; no or simplified representation of tides and river plumes, etc.
The model is presented in Sect. 2. Its capability to represent ocean dynamics and water masses is evaluated in Sect. 3. We then used this model to examine the variability of the SVU at different scales. Here we first focus on its interannual variability over its four main areas of development, examining in particular the role of high-frequency and small-scale dynamics. This analysis is presented in Sect. 4. Results are discussed in Sect. 5 and summarized in Sect. 6. 2 Methodology: numerical model and observation datasets 2. 1 The regional ocean hydrodynamical model SYMPHONIE The 3D ocean circulation model SYMPHONIE  (Marsaleix et al. , 2008 (Marsaleix et al. , , 2019) )  is based on the Navier-Stokes primitive equations solved on an Arakawa curvilinear C grid under the hydrostatic and Boussinesq approximations.
Interannual variability of surface circulation, temperature, and salinity VNC and SVU regions show very similar interannual chronologies of SST, SSS, and SLA (Fig. 7 , right panels). The interannual variability of SST is very well simulated, with a 0. 95 (p<0. 01) correlation coefficient and a 0. 13 NRMSE compared to the OSTIA dataset for VNC and, respectively, 0. 98 (p<0. 01) and 0. 06 for SVU (Fig. 7b, h  The simulated interannual SSS time series shows a statistically significant correlation (0. 80 over VNC, 0. 86 over SVU, p<0. 01) and a NRMSE of 0. 48 for VNC and 0. 39 for SVU compared to SMOS observations (Fig. 7d, j ). Both model and data show an increase between 2012 and 2016, also observed by  Zeng et al. (2014 Zeng et al. ( , 2018)). Through a budget analysis based on an SCS 4 km SYMPHONIE configuration over the SCS, https://doi.
org/10. 5194/os-18-1131-2022 Ocean Sci. , 18, 1131-1161, 2022   respond , respectively, to a spatial coverage of BoxMK lower than 50 %, higher than 50 %, and higher 60 % for JAXA. We only consider JAXA pixels of quality levels ≥ 4. Trinh (2020)  attributed this salinification to the rainfall deficit during this period. The interannual variability of SLA is very well simulated, with a 0. 94 (p<0. 01) correlation coefficient for both VNC and SVU and an NRMSE of, respectively, 0. 13 and 0. 12 compared to the AVISO dataset (Fig. 7f, l ). Both observed and simulated datasets show minimum values in  2009, 2011, and 2015, and maximum values in 2010, 2012, and 2013 , with an amplitude of ∼ 3-4 cm.
Representation of T S characteristics and vertical distribution of water masses at daily to interannual scales In Sect. 4 below, we use surface variables to quantify the SVU strength and investigate the factors involved in its interannual variability. Surface dynamics are however influenced by the ocean vertical stratification and dynamics. We therefore also assess the capability of the model to realistically reproduce the tridimensional evolution of water masses in the computational area from the open-sea region to the coastal areas. For that, we use the ARGO, GLIDER, IO-18, and ALIS-TSG datasets. We first examine T S diagrams built from ARGO (Fig. 8a ), GLIDER (Fig. 8b ), and IO-18 (Fig. 8c ) observations and co-localized SYMPHONIE outputs. At the regional scale, ARGO data cover the whole opensea region over 2009-2018.
GLIDER data were collected between January and May 2017, i. e. , during the period of northeast monsoon and the transition period to the southwest monsoon. They cover the open-sea region and the coastal area (Fig. 1b ). During this ninth year of the simulation, the model is able to reproduce in detail the observed temperature and salinity profiles (Fig. 8b ), though with slightly higher biases of salinity (up to 0. 15) and temperature (up to 0. 4 • C) than for the ARGO dataset. In particular it reproduces realistically not only the deep-water masses down to 1000 m depth but also the two branches of surface water masses that were sampled by the glider (Fig. SM1 ): the fresher branch (SSS < 34. 5) observed at the beginning of the glider cruise south of 16 • N and the warmer branch (reaching 20 • C) observed at the end of the cruise north of 17 • N.
Comparison of simulated and observed interannual variability of upwelling intensity Ngo and Hsin (2021) produced the most recent and longest time series of observed intensity of upwelling over three of the areas studied here (SCU, NCU, and OFU), using an SSTbased upwelling index applied to a 0. 25 • dataset of satelliteobserved SST over the period 1982-2019 (see Fig. 4  of  Ngo and Hsin, 2021). For SCU and OFU, the interannual variability of upwelling intensity over the period 2009-2018 is similar at the first order in the time series simulated by our model (Fig. 13 ) and observed by  Ngo and Hsin (2021). In particular their intensity is highest in 2018 and lowest in 2010. For SCU, 2011, 2012, and 2014 are more intense than the average in both simulated and observed time series, and 2017 is much less intense than the average.
Conclusions The purpose of this study was to simulate the large range of scales of ocean processes and atmospheric forcing, including submesoscale dynamics and high-frequency variability, in order to understand better their role in functioning and variability of the SVU. For that, we implemented a South China Sea configuration of the 3D ocean model SYMPHONIE at high resolution, varying from ∼ 1 km along the Vietnamese coast to ∼ 4 km in the open SCS, and ran a simulation over the period 2009-2018. Comparisons with available satellite data and four sets of in situ observations quantitatively showed the capability of the model to reproduce the ocean circulation and water masses at different scales in the area: from the daily to interannual and climatological scales and from the coastal to offshore regions.
Acknowledgements. This work is a part of LOTUS international joint laboratory (http://lotus. usth. edu. vn, last access: 25 July 2022). PhD studies of Thai To Duy were funded through an  IRD   ARTS grant  and a " Bourse d'Excellence " from the  French Embassy in Vietnam. Numerical simulations were performed using  CALMIP HPC facilities  (project  P13120 ) and the cluster  OCCIGEN  from the  CINES group  (project  DARI A0080110098 ). The  IO-18  campaign belongs to the " Study on processes of air-sea-land interactions and environmental variations in Bien Dong in the context of climate change within the framework of IOC\WESTPAC" project ; code:  DTDL. CN-28/17. This paper is a contribution to celebrate the 100-year anniversary of the  Institute of Oceanography, Vietnam Academy of Science and Technology.
, 2018 (Lundqvist et al. , , 2016;; Brockmeier and Principe, 2016). While the evidence discussed so far comes from experimentalists generating novel empirical data, computational and theoretical neuroscientists have also investigated these properties. For example, Lundqvist and collegues  (Lundqvist et al. , 2010)  examine how bistable, irregular firing and population oscillations arise in a modular attractor memory network. Similarly, Tiesinga and Sejnowski  (Tiesinga and Sejnowski, 2010)  explored how a set of spiking-neuron simulation models can account for the phase-shifting seen in empirical experiments. In contrast, the spiking network model described here operates according to different mechanisms than the two studies discussed above. The novel, simple, and minimal network model constructed in this paper -using the Python-based neural simulation package Brian2 -shows how the property of tunable phase shifting in multiple brain areas can be implemented via a small distributed network of spiking neurons.
While this model is simple by design, it is build using biophysically-realistic point neurons and plausible patterns of connectivity which can be scaled to larger studies. This study is a work of synthesis, combining the empirical and simulation findings of 4 prior papers. In the sections that follow, we first describe the 4 core elements of this model, introducing each with a brief summary of the key findings of the papers. Next, we use the Python-based neural simulation package Brian2 to model each of these 4 elements, both separately and in combination. After the final model is constructed and demonstrated through simple examples, we discuss the perils and promises of such a model -what the primary weaknesses of the model are, how similar models could be used to control larger artificial or biological networks of spiking neurons (artificial intelligence and brain-machine interfaces), and how similar models could be used to influence different coding schemes -such as asynchronous or firing rate-based coding schemes, as well as synchronous coding schemes including spike-timing dependent synfire chains.
The binning is done at the light-curve level before the fitting of the transit depths that constitute the transmission spectrum. Analyses of JWST/NIRSpec transit observations obtained during commissioning have shown that similar levels of binning as we use here results in minimal systematics  37. An analysis of the complete PRISM dataset at full resolution, including recovery of the saturated part of the spectrum, is ongoing. We reduced the NIRSpec PRISM data for WASP-39b using the JWST Science Calibration Pipeline along with customized routines to minimize noise in the time-series spectra (Methods). We performed four different reductions of the transmission spectrum starting from the uncalibrated data  21, [38] [39] [40]. Figure  1  shows derived spectroscopic transit light curves from one of the reductions.
2  demonstrates both the consistency in the derived spectra from multiple, independent analyses and the reliability of the previous Spitzer measurements. We compared the data with a suite of one-dimensional atmospheric structure and transmission spectrum models to constrain the composition of WASP-39b's atmosphere. These models assume radiativeconvective-thermochemical equilibrium, and they adopt a scaled  solar abundance pattern. We calculated planet-specific grids of these models over a range of atmospheric metallicities, carbon-to-oxygen ratios and cloud properties using four different codes. These grids of self-consistent model transmission spectra were then fitted to the FIREFLy-reduced data (the fit results are independent of which dataset we use) while also adjusting for a reference radius at 1 bar.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-022-05269-w. ) No H 2 O No CO No CO 2 No H 2 S No CH 4 H 2 O C O C O 2 H 2 S CH 4 Fig. 3  | Interpretation of WASP-39b's transmission spectrum. Top: a comparison of the FIREFLy reduction and its 1σ uncertainties (labelled 'Data') to the best-fit ScCHIMERA theoretical model binned to the resolution of the data (blue curve; Methods). The key parameters of the model are 10-times solar metallicity, a carbon-to-oxygen ratio of 0. 35 and cloud opacity of 7 × 10 -3 cm 2 g -1.
22. We also used the ratio a/R * (where a is the semi-major axis and R * is the stellar radius) from ref. 22  but widened the uncertainty on this parameter because the enforced prior resulted in significant residuals. Next, we fit each spectroscopic light curve individually with the orbital parameters fixed at the value from the white-light posterior medians. We modelled the light curves using the 'exoplanet' code  51  and the pymc3  52  sampler. We evaluated the wavelengths using the JWST Calibration pipeline at pixel row 16 (Y = 16) from the world coordinate solution. This uses an instrument model and could not be verified owing to a lack of strong stellar absorption features at the NIRSpec resolution. All the other reductions adopted this wavelength calibration. As shown in Fig. 1 , the standard deviation in the out-of-transit light curve approaches the theoretical limit of photon and read noise at short wavelengths, but is 20% to 40% higher at longer wavelengths, which may be related to uncorrected 1/f noise.
Eureka! pipeline Eureka! 39 is a data reduction and analysis pipeline for time-series observations with the JWST or the Hubble Space Telescope Its modular, multi-stage design provides flexibility and ease of comparison at any step, starting from uncalibrated FITS files and resulting in precise transmission or emission spectra. Eureka! has been used to successfully analyse the JWST transit observations of HAT-P-14b that were obtained during commissioning with NIRCam  37. We began the data reduction process using the uncalibrated raw data files with the "uncal" suffix available from the Mikulski Archive for Space Telescopes (MAST) archive. The first stage of the Eureka! pipeline is primarily a wrapper for Stage 1 of the JWST Calibration pipeline, which converts groups to slopes. For this dataset, we skipped the jump detection step as it led to a large fraction of detector pixels being incorrectly flagged as outliers.
We first generated median-normalized light curves at the instrument's native resolution (that is, from each detector column) using our Stage 3 outputs. We then clipped additional outliers in time for the white and spectroscopic light curves. For this step, we first rejected integrations that were more than 3σ outliers for the source position in the cross-dispersion direction, the width of the fitted Gaussian to the spatial profile or the drift in the dispersion direction. Next, we produced a median-filtered version of the light curve and clipped out 3σ outliers in flux. We jointly fit astrophysical and systematics model parameters to the white and individual spectroscopic light curves. Our astrophysical transit model used the batman package  56  with uniform priors, fitting for the following astrophysical parameters: the two coefficients of a stellar quadratic limb-darkening law, impact parameter, semi-major axis, transit time and the planet-to-star radius ratio (R p /R * ) in each of the wavelength channels.
Tiberius pipeline We built on the pipeline developed for the analysis of the Low Resolution Ground-Based Exoplanet Survey using Transmission Spectroscopy (LRG-BEASTS) data  21, 57, 58  to provide an independent reduction of the data. We began with the outputs of the JWST Calibration Stage 1 pipeline with the jump step correction turned off. We created bad-pixel and cosmic-ray masks by identifying 5σ outliers in running medians operating along pixel rows and along individual pixels in time. Before tracing the spectra, we interpolated each column of the detector onto a finer grid, ten times the initial spatial resolution, to improve the extraction of flux at the subpixel level. We used a fourth-order polynomial to trace the spectra and a four-pixel-wide aperture. To remove the 1/f noise, we fit a linear polynomial to 21 background pixels along each column in the cross-dispersion direction.
Acknowledgements This work is based on observations made with the  NASA/ESA/CSA James Webb Space Telescope. The data were obtained from the  Mikulski Archive for Space Telescopes at the Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127  for JWST. These observations are associated with programme  JWST-ERS-01366. Support for programme JWST-ERS-01366  was provided by  NASA  through a grant from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127. The results reported herein benefited during the design phase from collaborations and/or information exchange within  NASA's Nexus for Exoplanet System Science (NExSS)  research coordination network sponsored by  NASA's Science Mission Directorate.
Data availability The data used in this paper are associated with JWST programme ERS 1366 (observation #4) and are available from the Mikulski Archive for Space Telescopes (https://mast. stsci. edu). Science data processing version (SDP_VER) 2022_2a generated the uncalibrated data that we downloaded from MAST. We used JWST calibration software version (CAL_VER) 1. 5. 3 with modifications described in the text. We used calibration reference data from context (CRDS_CTX) 0916, except as noted in the text. All the data and models presented in this publication can be found at https://doi. org/10. 5281/zenodo. 6959427. Source data are provided with this paper.
Code availability The codes used in this publication to extract, reduce and analyse the data are as follows: STScI JWST Calibration pipeline  37  (https://github. com/spacetelescope/jwst), tshirt  40  , Eureka! 39 (https://eurekadocs. readthedocs. io/en/latest/), Tiberius  21, 56, 57  and FIREFLy  38. In addition, these made use of Exoplanet  51  (https://docs. exoplanet. codes/en/latest/), Pymc3  52  (https://docs. pymc. io/en/v3/index. html), ExoTEP  [53] [54] [55]  , Batman  56  (http://lkreidberg. github. io/batman/docs/html/index. html), ExoTiC-ISM  59  (https://github. com/Exo-TiC/ExoTiC-ISM), ExoTiC-LD  60  (https://exotic-ld. readthedocs. io/en/latest/), Emcee  63  (https://emcee. readthedocs. io/en/stable/), DYNESTY  74  (https://dynesty. readthedocs. io/en/stable/index. html) and chromatic (https://zkbt.
github. io/chromatic/), each of which use the standard Python libraries scipy  98  , numpy  99  , astropy  100, 101  and matplotlib  102. The atmospheric models used to fit the data are as follows: PICASO  84  (https://natashabatalha. github. io/picaso/), Virga  86  (https://natashabatalha. github. io/virga/), ScCHIMERA  68  (https:// github. com/mrline/CHIMERA), ATMO  [88] [89] [90] [91]  and PHOENIX  94.