dataesr/scientific-paragraphs-categorization huggingface dataset:
A "best case" standard uncertainty in CTD thermistors of ±0. 001 • C is indicated in Le Menn (  2011 ), and we refer the reader to this paper for further detail on uncertainty in conductivity and salinity in CTDs. IQuOD assigns a value of ±0. 01 • C to uncalibrated CTD data and CTD data with no indication of calibration status (including low resolution CTD data in the OSD collection) following  Atkinson et al. (2014). Towed and undulating CTD data collected from moving vessels are categorized under the Undulating Oceanographic Recorders (UOR) section of the WOD. Recorders included in the UOR category include CTDs, Continuous Plankton Recorders (CPR) and some Oxygen recorders  (Boyer et al. , 2018b). For CTD data collected in the UOR category, the SBE 911 was the most common recorder used and many of the datasets have good documentation.
Uncertainty could be reduced for some of these data after review. CTDs have been attached to/mounted on a variety of animals including fish, sharks, seals and other mammals. In the WOD and IQuOD v0. 1, only pinniped mounted data are included. These data are assigned a value of ±0. 005 • C after  Atkinson et al. (2014) , however, higher ranges are noted for different sensors (Table  1 ). We have not incorporated higher uncertainty values in IQuODv0. 1, but in future releases these values will be applied to specific sensors.
A study completed in 1993  (Elgin, 1994)  on Sippican XCTDs with a Sippican MK 12 system found a standard deviation of ±0. 012 • C. In the late 1990s, TSK and Sippican reached an agreement to market the TSK XCTD probe only, and Sippican ceased manufacture of their XCTD  (Hannon, 2000). However, it is likely there is data from the Sippican XCTDs up to 2003 in the WOD. The manufacturer information for XCTDs is not always supplied to the WOD and IQuOD v0. 1 does not differentiate probes by manufacturer when assigning uncertainties for XCTDs. Obtaining and reviewing original datasets could assist with improving the number of XCTDs with probe type information, and therefore refining the uncertainties associated with each profile. In addition, identification of countries that only deployed a specific type (e.
Micro BT (Micro BathyThermograph) Micro BT instruments are a small collection of data in the WOD. Both RBR and Seabird manufactured these instruments, which have a rapid response thermistor and a strain gauge pressure sensor and are attached to nets, cables or towed vehicles  (Boyer et al. , 2018b). The uncertainty selected for all micro BT instruments in IQuOD v0. 1 (±0. 002 • C) is the accuracy for Seabird instruments  (Boyer et al. , 2018b). RBR quotes a resolution of 0. 1 • C, which indicates a larger uncertainty could apply to profiles from RBR, however the metadata is not always available to distinguish between the two manufacturers.
Moored Buoy The moored buoy data in the WOD are sourced from various programs including the Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON), Pilot Research Moored Array in the Tropical Atlantic (PIRATA), Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA), and Arctic/Subarctic Ocean Fluxes (ASOF) programs  (Boyer et al. , 2018b). The TAO/TRITON, RAMA and PIRATA arrays are part of the Global Tropical Moored Buoy Array (GTMBA). Atkinson et al. (2014)  assign a temperature uncertainty for data sourced from GTMBA and WOD of ±0. 02 • C and for other moored buoy datasets, uncertainty is set to ±0. 36 • C. ICOADS 2. 5. 1 assigns an uncertainty of ±0. 30 • C to surface temperatures from moored buoys  (Woodruff et al. , 2011). Moored buoys are capable of carrying a large range of instrumentation.
, 2021). Some profiling floats released in the late 1990s (early in the development of the Argo program) are available from the Argo GDACs and so are considered part of the Argo program. Argo floats are differentiated from early profiling floats as they follow the Argo program specifications and regulations. Argo floats are identified in the WOD with second header code 91, "source database" code=7 [U. S. GODAE server (Argo)].
Discussion Uncertainty values (combined standard uncertainties) have been assigned to every temperature data point in the IQuOD v0. 1 release and are available through the NCEI website via a complete download of IQuOD v0. 1 (https://doi. org/10. 7289/ v51r6nsf) or the WOD Select online tool (https://www. ncei. noaa. gov/access/world-ocean-database-select/dbsearch. html). Uncertainties were assigned after careful consideration of manufacturer specifications and other references, and when considering GUM recommendations, these are assigned as Type B uncertainties. The WOD does not contain uncertainty information. IQuOD v0. 1 fills the gap by providing initial estimates of uncertainty. Although these uncertainty estimates can be improved and will be updated in future IQuOD releases, the first step has been made in re-constructing uncertainty estimates in the v0.
1 product. Bushnell et al. (2019)  presented an excellent overview of best practices in oceanographic measurements, including uncertainty estimation from Type B sources. In future releases of IQuOD, uncertainty estimates could be much improved upon by following the best practice methods outlined by  Bushnell et al. (2019). The IQuOD project's mission is to provide a uniformly expert quality controlled ocean temperature profile dataset. In doing so, IQuOD will enhance historical ocean temperature profile data with retrieved metadata, addition of intelligent metadata, addition of uncertainty estimates and bias corrections. The IQuOD team and associates will continue to compile and rescue metadata to assist with improving uncertainty estimates and bias corrections. Bias corrections are already available for XBT data and the  (Cheng et al.
, 2014)  XBT bias correction is the default scheme applied in IQuOD v0. 1. Improvements in bias corrections for XBT and other data (e. g. , MBT) will be applied to future IQuOD releases to enhance the quality of the historical temperature profile dataset. Although biases and uncertainty are treated separately in IQuOD and uncertainty is estimated independently of any biases in data, uncertainty values could be estimated where known biases exist. There are several high quality datasets of ocean profile data (e. g. , Hydrobase, WOCE hydrographic and GO-SHIP datasets) that, once incorporated with full metadata and scientific quality flag information, will allow more specific estimates of uncertainty for these data. In future IQuOD database versions, we expect uncertainties will also be a function of institute, country, project, and known processing practices, particularly where we are unable to source precise uncertainty information.
Machine learning techniques, similar to those used in  Leahy et al. (2018) , might be employed to assist with uncertainty assignment based on available metadata. Retrieval of metadata such as calibration information, instrument type and uncertainty specifics from original cruise reports and institutional documents, although a large task, would reduce the need for such techniques to assign missing information. We encourage and invite submissions of such valuable information to the IQuOD project and the WOD. Additional future improvements of the uncertainty characterization in the IQuOD database might include several other updates, aiming at adding specific error components for use in data assimilation and, more specifically, in the production of ocean reanalyses. These additional error components, discussed below, might be provided separately to ease their use by the data assimilation users.
Consequently, it is customary that data assimilation systems embed algorithms for characterizing the representativeness error. The IQuOD v0. 1 product could be used to derive estimates of vertical representativeness errors, which are primarily associated with the sampling rate of the instrument (i. e. , its vertical resolution), the location of mixed layer and thermocline, and therefore can be assessed for each individual profile (e. g. ,  Penny et al. , 2015). This can be done either using boxcar-averaged profiles to calculate the vertically varying representativeness error  (Oke and Sakov, 2008)  or through analytical relationships based on the vertical gradients  (Penny et al. , 2015)  or, alternatively, using ensemble ocean model realizations  (Karspeck, 2016). Another use for the IQuOD product would be to derive information about horizontal scale representativeness for spatial analysis of temperature fields based on the Objective Analysis algorithm.
FUNDING This work was supported by the  Scientific Committee on Oceanic Research (SCOR)  Working Group 148, funded by national SCOR committees and a grant to SCOR from the  U. S. National Science Foundation  (Grant  OCE-1546580 ); and the  Intergovernmental Oceanographic Commission of UNESCO/International Oceanographic Data and Information Exchange (IOC/IODE) IQuOD Steering Group. RC was supported through funding from the  Earth Systems and Climate Change Hub of the Australian Government's National Environmental Science Program. RK and MP were supported by the  Met Office Hadley Centre Climate Programme  funded by  BEIS and Defra. CD was supported by the  Australian Research Council  ( Discovery Grant   DP160103130 ),  ARC Centre of Excellence for Climate Extremes  ( CE170100023 ) and by the  Natural Environment Research Council  (TICTOC,  NE/P019293/1 ).
We present the discovery and extensive follow-up observations of SN 2020jfo, a Type IIP supernova (SN) in the nearby (14. 5 Mpc) galaxy M61. Optical light curves (LCs) and spectra from the Zwicky Transient Facility (ZTF), complemented with data from Swift/UVOT and near-infrared photometry is presented. These are used to model the 350-day duration bolometric light curve, which exhibits a relatively short (∼ 65 days) plateau. This implies a moderate ejecta mass (∼ 5 M ) at the time of explosion, whereas the deduced amount of ejected radioactive nickel is ∼ 0. 025 M. An extensive series of spectroscopy is presented, including spectropolarimetric observations. The nebular spectra are dominated by Hα but also reveal emission lines from oxygen and calcium. Comparisons to synthetic nebular spectra indicate an initial progenitor mass of ∼ 12 M.
We also note the presence of stable nickel in the nebular spectrum, and SN 2020jfo joins a small group of SNe that have inferred super-solar Ni/Fe ratios. Several years of pre-discovery data are examined, but no signs of pre-cursor activity is found. Pre-explosion Hubble Space Telescope imaging reveals a probable progenitor star, detected only in the reddest band (MF814W ≈ -5. 8) and is fainter than expected for stars in the 10 -15 M range. There is thus some tension between the LC analysis, the nebular spectral modeling and the pre-explosion imaging. To compare and contrast, we present two additional core-collapse SNe monitored by the ZTF, which also have nebular Hα-dominated spectra. This illustrates how the absence or presence of interaction with circumstellar material (CSM) affect both the LCs and in particular the nebular spectra.
X-rays The field of SN 2020jfo was observed with Swift's onboard Xray telescope (XRT;  Burrows et al. 2005)  in photon-counting mode several times between 2020 May 7 and 2021 January 30. Swift also observed this field between 2008 and 2016, long before the SN explosion. We analysed all data with the online tools of the UK Swift team 6 that use the methods described by  Evans et al. (2007)  and  Evans et al. (2009)  and the software package HEAsoft. We detect emission at the SN position in the 2008-2016 datasets. To recover any SN flux in the later data, we numerically subtracted that baseline flux, but this did not result in any significant detections (< 1. 4σ). To convert count-rate limits to flux, we use WebPIMMS 7 , assume a power law with a photon index of 2 and a Galactic equivalent neutral-hydrogen column density of 1.
To locate the precise position of the SN in the archival HST datasets, we obtained high-spatial-resolution images in the K Fig. 2 : Light curves of the three supernovae. Left: SN 2020jfo in multiple bands with symbols and offsets as provided in the legend to the very right. In this case it includes six passbands from Swift and three from ZTF and LT, as well as near-infrared J-band data. These are observed (AB) magnitudes plotted versus rest-frame time in days since explosion. For the J-band data, the Vega/AB magnitude conversion follows  Blanton & Roweis (2007). ATLAS forced photometry in the c and o bands is also included, and the ZTF and ATLAS data are in 3 day bins. The arrows on top indicate the epochs of spectroscopy, and the lines with error regions are Gaussian Process estimates of the interpolated LCs (for g band).
The middle and right panels show the LCs for SN 2020amv and SN 2020jfv, respectively. band with the adaptive optics (AO)-assisted OSIRIS Imager  (Larkin et al. 2006 ) on the Keck-I 10 m telescope on 2020 July 7. The SN field required laser-guide-star AO and short-turnaround clearance for satellite avoidance. There were 10 frames added together, with each coadd consisting of 8 dithered images of duration 14. 75 s, so the total integration time was 1180 s. We astrometrically registered the Keck AO image to the 2012 ACS/WFC F814W image using 32 stars in common between the two image datasets, employing the task geomap in PyRAF with parameter "calctype" set to "double. " The geometric distortion for the OSIRIS AO imager is quite small (<5 milliarcsec), so a distortion correction is not strictly required 11.
CSM interaction is likely also the reason why SN 2020jfv rebrightens hundreds of days past explosion. This SN IIb initially did not have strong signatures of hydrogen, whereas the nebular spectrum is completely dominated by Balmer lines. The line shapes are somewhat intermediate between those discussed for the other two SNe above. Instead of evidence for CSM interaction in terms of a broad, flattopped line profile, we see an intermediate-width asymmetric line, where optical-depth effects likely play a role in shaping the line. Interest in both of the latter targets was raised only after we realised their slowly evolving and rebrightening light curves, and in both cases initial SEDM data convinced us to also obtain spectra at larger telescopes. SEDM can thus operate both as a classification machine and as a science data provider (as for SN 2020jfo), but also as a way to investigate whether additional observations should be undertaken for any given transient.
The challenge has moved to being able to digest and publish the available data, and to detect, select, and monitor events of particular interest. SNe 2020amv and 2020jfv were both unusually long lived with LCs that rebrightened. Dedicated spectroscopic follow-up observations were required to confirm CSM interaction as the powering mechanism for these objects. were obtained for their excellent assistance. Based on observations obtained with the Samuel Oschin Telescope 48-inch and the 60-inch Telescope at the Palomar Observatory as part of the Zwicky Transient Facility project. ZTF is supported by the U. S. National Science Foundation (NSF) under grant AST-1440341 and a collaboration including Caltech, IPAC, the Weizmann Institute for Science, the Oskar Klein Center at Stockholm University, the University of Maryland, the University of Washington, Deutsches Elektronen-Synchrotron and Humboldt University, Los Alamos National Laboratories, the TANGO Consortium of Taiwan, the University of Wisconsin at Milwaukee, and Lawrence Berkeley National Laboratories.
ZTF-II is supported by the National Science Foundation under Grant No. AST-2034437 and a collaboration including Caltech, IPAC, the Weizmann Institute for Science, the Oskar Klein Center at Stockholm University, the University of Maryland, Deutsches Elektronen-Synchrotron and Humboldt University, the TANGO Consortium of Taiwan, the University of Wisconsin at Milwaukee, Trinity College Dublin, Lawrence Livermore National Laboratories, and IN2P3, France. Operations are conducted by COO, IPAC, and UW. The SED Machine is based upon work supported by the NSF under grant AST-1106171. The ZTF forced-photometry service was funded under the Heising-Simons Foundation grant #12540303 (PI M. Graham). This work was supported by the GROWTH project funded by the NSF under PIRE grant 1545949. The Oskar Klein Centre was funded by the Swedish Research Council.
Gravitational Radiation and Electromagnetic Astrophysical Transients (GREAT) is funded by the Swedish Research council (VR) under Dnr 2016-06012. ECK is further supported by the Wenner-Gren Foundations. Much support to OKC involvement in ZTF was provided by the Knut and Alice Wallenberg foundation. Partially based on observations made with the Nordic Optical Telescope, operated by the Nordic Optical Telescope Scientific Association at the Observatorio del Roque de los Muchachos, La Palma, Spain, of the Instituto de Astrofisica de Canarias. Some of the data presented here were obtained with ALFOSC. Based on observations made with the NASA/ESA Hubble Space Telescope, obtained from the data archive at the Space Telescope Science Institute (STScI). STScI is operated by the Association of Universities for Research in Astronomy, Inc.
under NASA contract NAS 5-26555. Support for program GO-16179 was provided by NASA through a grant from STScI. We thank T. de Jaeger, O. D. Fox, P. Kelly, N. Smith, S. Vasylyev, and W. Zheng for their assistance with this program. Some of the data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and NASA; the observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain.
For the polycrystal behavior, the relaxed constraints Taylor-type approach was employed. The experimental texture after FALEP (Figure  6b, d ) was discretized to 5000 grain orientations with the help of the ATEX software version 2. 21 (Metz, France). [37]. Ideal fiber textures as well as all ideal shear texture components were also generated by the ATEX software containing each 5000 grain orientations in a 20 • Gaussian spread distribution around the ideal positions. Tension test was simulated by imposing the tensile strain rate. ε in direction of axis 1, while leaving the perpendicular strain rate components free (. ε 22 ,. ε 33 ), that is, they were relaxed. The. ε 12 shear component was also relaxed, which is a shear permitted to relax by the boundary conditions for a tensile test. The R-value is defined by the ratio: R =.
During the optimization, combinations of hyperparameter values were iteratively sampled, each evaluated over 10 trials with different random seeds, for a total of 60 iterations (hence, 600 trials) for a given architecture, environment, and number of units. In each trial, one network was created, trained, and its cross-entropy was measured on independent test data. The training and test datasets used for the hyperparameter optimization procedure were not used in any other analyses. The training datasets contained respectively 160 and 400 minibatches of 20 sequences for the unigram and the bigram environment; the test datasets contained 200 sequences for each environment. We selected the combination of hyperparameter values corresponding to the iteration that led to the lowest mean test cross-entropy over the 10 trials.
Performance analyses All agents were tested in the environment they were trained in (except for Figure  6 -figure supplement 1 which tests cross-environment performance). We used a single test dataset per environment of 1000 sequences independent of the training dataset. The log likelihood L of a given agent was measured from its predictions according to Equation 1. The optimal log likelihood L optimal was measured from the predictions of the optimal solution for the given environment. The chance log likelihood L chance was measured using a constant prediction of 0. 5. To facilitate the interpretation of the results, the prediction performance of the agent was expressed as the % of optimal log likelihood, defined as: L-Lchance Loptimal-Lchance × 100 To test the statistical significance of a comparison of performance between two types of agents, we used a two-tailed two independent samples t-test with Welch's correction for unequal variances.
Readout analyses The readout of a given quantity from the recurrent units of a network consists of a weighted sum of the activation values of each unit. To determine the weights of the readout for a given network, we ran a multiple linear regression using, as input variables, the activation of each recurrent unit at a given time step h i,t , and as target variable, the desired quantity calculated at the same time step. The regression was run on a training dataset of 900 sequences of 380 observations each (hence, 342,000 samples). In the unigram environment, the precision readout was obtained using as desired quantity the log precision of the posterior distribution over the unigram variable calculated by the optimal solution as previously described, that is, ψt = -log σt , where σt is the standard deviation of the posterior distribution over p env t+1 : σt = SD[p env t+1 |x 0 ,.
, xt] (3) In the bigram environment, the readout of the estimate of a given bigram variable was obtained using as desired quantity the log odds of the mean of the posterior distribution over that bigram variable calculated by the optimal solution, and the readout of the precision of that estimate was obtained using the log precision of that same posterior under the above definition of precision. In Figure  4a , to measure the accuracy of the readout from a given network, we calculated the Pearson correlation between the quantity read from the network and the optimal quantity on a test dataset of 100 sequences (hence, 38,000 samples), independent from any training dataset. To measure the Pearson correlation between the read precision and the subsequent effective learning rate, we used 300 out-of-sample sequences (hence, 114,000 samples).
Statistics To assess the variability between different agent solutions, we trained 20 agents for each type of agent and each environment. These agents have different random seeds (which changes their parameter initialization and how their training data is shuffled). Throughout the article, we report mean or median over these agents, and individual data points when possible or 95% confidence intervals (abbreviated as "CI") otherwise, as fully described in the text and figure legends. No statistical methods were used to pre-determine sample sizes but our sample sizes are similar to those reported in previous publications  (Masse et al. , 2019; Yang et al. , 2019). Data analysis was not performed blind to the conditions of the experiments. No data were excluded from the analyses. All statistical tests were two-tailed unless otherwise noted.
Foucault and Meyniel. eLife 2021;10:e71801. DOI: https://doi. org/10. 7554/eLife. 71801 27 of 33 Additional information Funding Funder Grant reference number Author École normale supérieure PhD fellowship "Contrat Cédric Foucault Paris-Saclay doctoral spécifique normalien" Agence Nationale de la 18-CE37-0010-01 "CONFI Florent Meyniel Recherche LEARN" H2020 European Research ERC StG 947105 "NEURAL Florent Meyniel Council PROB".