dataesr/scientific-paragraphs-categorization huggingface dataset:
Sociodemographic items. The following socio-demographic data were collected to describe the sample: age, sex, postal code, annual income, subscription to car-sharing systems and level of education (More details are provided in Table  1 ). Perception of the cycling environment. We used nine items to evaluate the perception of the cycling environment. Those items were adapted from the "Active Commuting Route Environment Scale" for winter bicycle  (Wahlgren & Schantz, 2014)   TPB items. Attitude toward using winter bicycle commuting in the next 30 days was assessed with the two semantic differentials, good to bad and pleasant to unpleasant, each using a seven-point Likert scale (1-7). Respondents indicated their agreement to two items assessing subjective norms (e. g. , "Most of the people who are important to me think that I should take the bike to commute in the winter"); perceived behavior control (e.
Questionnaire 2 One month after the first questionnaire, a second questionnaire collected data on the use of winter bicycle commuting in the last seven days. These items were adapted from a survey of Vélo Québec  (Poirier et Thériault. , 2021). There were two items to assess (1) frequency, i. e. , how many times during the last seven days did the participants commute by bike, and (2) duration, i. e. , how many hours did the participants commute by bike. These items were used as dependent variable in our longitudinal analysis.
io/35ps7/). This study's design and its analysis were not pre-registered. The analyses were conducted with R software (4. 2 version), using the following packages: lavaan  (Rosseel, 2012) , semPlot, ggmap  (Kahle and Wickham, 2013) , ggplot2, stargazer, dplyr, and ggstatplot  (Patil, 2021). The lavaan script was based on the ShareSEM initiative  (Phipps, 2019).
Estimating the differences in the incubation-period, serial-interval, and generationinterval distributions of SARS-CoV-2 variants is critical to understanding their transmission. However, the impact of epidemic dynamics is often neglected in estimating the timing of infection-for example, when an epidemic is growing exponentially, a cohort of infected individuals who developed symptoms at the same time are more likely to have been infected recently. Here, we reanalyze incubationperiod and serial-interval data describing transmissions of the Delta and Omicron variants from the Netherlands at the end of December 2021. Previous analysis of the same dataset reported shorter mean observed incubation period (3. 2 d vs. 4. 4 d) and serial interval (3. 5 d vs. 4. 1 d) for the Omicron variant, but the number of infections caused by the Delta variant decreased during this period as the number of Omicron infections increased.
Methods A. Data. We analyze time series of reported COVID-19 cases (https://data. rivm. nl/covid-19/) and proportions of SARS-CoV-2 variants detected (https://www. rivm. nl/coronavirus-covid-19/ virus/varianten) from the Netherlands between 29 November 2021 and 30 January 2022. Datasets are publicly available on the National Institute for Public Health and the Environment (RIVM) website. Serial-interval data are taken from ref. 4. Infector-infectee pairs were identified through contact tracing, and their symptom onset dates were reported through a national surveillance database. Serial intervals were then calculated by taking the difference between symptom onset dates of the infector and the infectee. In order to ensure independence between serial intervals, one infectee was chosen at random for each infector in the original analysis.
In the main text, we combine data from weeks 50 and 51 of 2021 (13 to 26 December) and present a stratified analysis in SI Appendix. For simplicity, we refer to transmission pairs with and without SGTFs as Omicron and Delta transmission pairs, respectively. Incubation-period data were originally collected from 513 individuals (consisting of 258 Omicron and 255 Delta cases), with symptom onsets between December 1, 2021 and January 2, 2022; however, the data are not publicly available with the original article. Instead, we rely on previous estimates (4) to derive growth-rate-adjusted incubationperiod distributions.
B. Estimating Epidemic Growth Rates. In order to accurately estimate incubation-period and generation-interval distributions of the Delta and Omicron variants, we have to take their epidemiological dynamics-in particular, their growth rates-into account. To estimate the growth rates of the Delta and Omicron variants, we first estimate the number of COVID-19 cases infected with each variant by multiplying reported weekly numbers of cases by the proportion of Delta and Omicron variants detected-we use weekly time series to smooth over patterns of testing and reporting within each week. We note that the proportion of Delta and Omicron variants detected is reported with the date of sampling, whereas the case data are reported with the date of report, meaning that there is some delay between the two datasets (typically around 2 d).
04/d by the end of January 2022 (Fig. 2D ). The number of COVID-19 cases infected with the Omicron variant increased rapidly but decelerated over time with timevarying growth rates decreasing from r = 0. of lockdown on December 19, 2021  (24)  and its relaxation beginning January 15, 2022  (25, 26). We note that the growthrate difference between the Delta and Omicron variants decreased over time. Hereafter, we use r = -0. 05/d for the Delta variant and r = 0. 15/d for the Omicron variant as representative growth rates-these growth rates correspond to the mean growth rates between December 1, 2021 and January 2, 2022, during which the incubation-period data were collected. We then evaluate the growth-rate effects across r = -0. 1/d to 0. 0/d for the Delta variant and r = 0. 1/d to 0. 2/d for the Omicron variant as a sensitivity analysis.
Discussion We compare estimates of the forward incubation-period and generation-interval distributions of the Delta and Omicron variants from the Netherlands in late 2021 and early 2022. The original analysis detailing the dataset previously reported a shorter mean incubation period and serial interval for the Omicron variant (4). Accounting for differences in epidemic growth rates, however, we find similar incubation-period distributions for both variants but a shorter (by 0. 3 to 0. 8 d) mean generation interval for the Omicron variant relative to that of the Delta variant. Finally, we estimate that the transmission advantage of the Omicron variant decreased from 2. 1-to 1. 4-fold between early December and late January. Improving generation-interval estimates by taking dynamical effects into account may improve understanding of epidemic dynamics and control measures.
ACKNOWLEDGMENTS. We thank  Ron Milo  for providing helpful comments on the manuscript. J. D. was supported by the  Canadian Institutes of Health Research , the  Natural Sciences and Engineering Research Council of Canada , and the  Michael G. DeGroote Institute for Infectious Disease Research. J. S. W. acknowledges support from the  Army Research Office   W911NF1910384  and the  Ile de France region via the Chaires Blaise Pascal program. J. W. and J. B. have received funding from  European Union 's  Horizon 2020 research and innovation program -project  EpiPose, Epidemic Intelligence to Minimize COVID-19 's  Public Health, Societal and Economical Impact  (grant agreement no. 101003688 ). S. F. was supported by the  Wellcome Trust  (grant no. 210758/Z/18/Z ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The blue and green triangles represent the location of the GPS ARID and seismic stations, respectively. The digital elevation model and historical lava flows were downloaded from the public graphic repository of GrafCan (http:// www. grafc an. es). The 2021 lava flow was downloaded from the European agency Copernicus Emergency Management Service (https:// emerg ency. coper nicus. eu/ mappi ng/ list-of-compo nents/ EMSR5 46). The software used to generate this figure was QGIS 3. 22 (https:// www. qgis. org). complex, and a subaerial phase, which originated the big insular edifices conformed by Taburiente (from 1. 77 to 1. 20 Ma) and Bejenado (from 0. 56 to 0. 49 Ma) stratovolcanoes  18. The Dorsal Domain is more recent and currently volcanically active. It is located in the southern part of the island, south of the Taburiente Domain.
Methodology and data processing The seismic data used in this work come from the Red Sísmica Canaria (C7) operated by INVOLCAN (Instituto Volcanológico de Canarias, 2016). We used recordings from six broadband seismic stations (Nanometrics © Trillium Compact 120 s and Güralp ©, 3ESPC Series) with a sampling rate of 100 Hz (Fig. 1 ). The time range used for the analysis covers the interval from Aug. 1st to Sept. 25th, 2021. We analysed the data using the MSNoise python package  19  to estimate relative velocity variations. This software has been applied successfully in different studies of ambient noise interferometry  14, [20] [21] [22] [23].
The histogram bars indicate the seismicity possibly related to the fluid injection (green dots) and magmatic intrusion (black dots). The relative velocity variation curves were obtained using MSNoise software  19  (http:// www. msnoi se. org). that this area could have hosted a hydrothermal reservoir prior to the eruption, at a depth of about 2 km b. s. l. The area affected by the decrease in dv/v is mostly located between the station PLPI and PCOR, and extends approximately between 2 and 12 km south of the eruptive center (Fig. 4 ), coinciding with the previously identified hydrothermal reservoir. The source of these hydrothermal fluids can be ascribed to the ascending magma batch itself. The upward migration of hypocenters and the increase in the ground deformation clearly suggest that the magma was rising at least since Sept.
Acknowledgements This paper benefited from various Python packages including Matplotlib 40 , Obspy 41,42 and MSNoise 19. The INVOLCAN team was supported by the projects  VOLRISKMAC II  ( MAC2/3. 5b/328 ) co-financed by the  EC Cooperation Transnational Program   MAC 2014-2020  and  Cumbre Vieja Emergencia , financed by  Ministerio de Ciencia e Innovación of the Spanish Goverment. J. M. I and I. K. were supported by the project  FEMALE of the Spanish Government  (Grant No. PID2019-106260GB-I00 ). J. M. I. is founded by the  Spanish PROOF-FOREVER  ( EUR2022. 134044 ) project.
These APs are not only destined for Gaia releases, but they are also used internally in DPAC systems, e. g. for determining the radial velocity (RV) template in the RV data reduction and analysis  (Sartoretti et al. 2018). The CU8 Apsis pipeline was first described in Bailer-Jones, C. A. L. et al. (2013)  before the launch of Gaia. Apsis comprises 13 modules that use different input data and/or models to produce APs for sub-stellar objects, stars, galaxies, and quasars. In Gaia Data Release 2, only two of the thirteen modules processed data to produce five stellar parameters (T eff , extinction A G , colour-excess E(G BP -G RP ), radius R, luminosity L) based on parallaxes and integrated photometry  (Andrae et al. 2018). Now, in Gaia Data Release 3, all of the 13 Apsis modules processed data and have contributed to the catalogue to provide 43 primary APs along with auxiliary parameters that appear in a total of 538 archive fields.
More details on the validation and use of the stellar and non-stellar products can be found in the accompanying papers II  (Fouesneau 2022)  and III  (Delchambre 2022)  respectively. More complete descriptions of specific products and methods can be found in the following papers:  Delchambre (2018) ;  Andrae (2022) ;  Lanzafame (2022)  and  Recio-Blanco (2022b) , and the official online documentation 1. The AP content of Gaia DR3 represents one of the most extensive homogeneous databases of APs to date for exploitation in many domains of astrophysics, see e. g. Bailer-Jones (2022) ;  Creevey (2022) ;  Drimmel (2022) ; Recio-Blanco (2022a);  Schultheis (2022). This paper is structured as follows: Sections 2, 3, and 4 describe the input data, provide an overview of the methods used in Apsis, and describe the stellar models, respectively.
Input astrometry and photometry We used the proper motions and parallaxes from Gaia in the processing of some of the Apsis modules. As some stellar-based modules are sensitive to the parallax zero-point, we implemented the systematic correction to the parallaxes as proposed by  Lindegren et al. (2021) , who reports biases that vary with magnitude, colour, and ecliptic latitude. Some of the Apsis modules use integrated photometry in the G, G BP and/or G RP bands, using the zero-points provided directly by the Coordination Unit 5 (CU5) in Gaia eDR3  (Riello et al. 2021). They also recommended to correct some of the Gaia eDR3 photometry, and this was implemented in our processing. This same correction to the eDR3 photometry has been fixed in Gaia DR3  (Gaia Collaboration et al. 2021a). Figure  2  shows the distribution in G magnitude of all of the individual products from CU8 from the four 1D tables.
BP and RP spectra Most of the Apsis modules produce APs based on the mean blue and red prism spectra (BP and RP), which are available for all of the sources in Gaia DR3. Examples of these prism spectra are shown in Fig. 4  for stars with different T eff and extinction (A 0 ). These mean low resolution spectra (20 ≤ R ≤ 60 for BP, 30 ≤ R ≤ 50 for RP, see Fig. 18  of  Montegriffo et al. 2022 ) allow one to extract the atmospheric parameters (T eff , log g, A G , [M/H]), but also contain enough resolution to explore specific O. L. Creevey et al. :  Overview of astrophysical parameters in Gaia DR3 Fig. 1. Apsis workflow showing the input data (colour-code) used by the 13 modules producing APs in Gaia DR3 along with the dependencies among these modules (arrows). The input BP/RP spectra in Apsis are in the form of sampled spectra, produced by SMSgen, see Fig.
Sampled Mean Spectrum Generator (SMSgen) The modules in the Apsis pipeline use the internally calibrated mean BP/RP spectra in the format of sampled spectra (integrated flux vs. pixel). Computing these sampled spectra from the CU5 coefficients is the task of the Sampled Mean Spectrum Generator (hereafter SMSgen). To this end, SMSgen takes the CU5 definition of the basis functions and integrates the spectral flux densities for a fixed wavelength grid. This wavelength grid defines 120 pixels for each BP and RP spectrum that cover the range of non-zero transmission in each spectrum as shown in Fig. 5   3. Here, we use the most recent EDR3 passbands  4. The wavelength sampling is approximately uniform in pixel space but non-uniform in wavelengths. SMSgen then numerically integrates the flux densities in order to obtain integrated fluxes in each pixel.
Note that BP/RP spectra can exhibit non-zero flux in pixels which have no transmission due to the LSF smearing effect of the BP/RP prisms, although this is negligible in practice. Apsis modules using BP/RP spectra anyway discard several pixels at the edges that typically have very low flux and very low SNR. The sampling process of the CU5 basis functions as well as the flux integration are strictly linear operations. Consequently, SMSgen can easily propagate the CU5 uncertainty estimates on the coefficients into uncertainties of the sampled BP/RP spectrum. However, Apsis modules currently ignore any correlations between pixels, so SMSgen only provides standard deviations for the flux uncertainties of each pixel. This approximation en-A&A proofs: manuscript no. apsis1 Lower: The sources with a CU8 result in the qso_candidate or galaxy_candidate tables (blue/orange) and the sources in those tables with a redshift from QSOC (green) or UGC (red).
sures lower computational cost, which is a limiting factor during CU8 operations. Unfortunately, as illustrated in Fig. 6 , notable long-range correlations between pixels do exist in BP and RP spectra (see  Babusiaux et al. 2022). Ignoring these correlations therefore causes several Apsis modules to systematically underestimate the uncertainties in their parameters, although most modules have inflated their uncertainties to account for this effect.
Parameter estimation methods The Apsis chain produces all of the data from CU8 for Gaia DR3. Apsis is composed of 14 modules, 13 of which produce data for the release. All of the modules are described individually and in more technical detail in Section 3 of Chapter 11 Astrophysical Parameters in the online documentation for Gaia DR3. The first module providing the BP/RP spectra in the CU8 format (SMSgen) is summarised above in Sect. 2. 3. 2. Here, we provide a brief overview of the other modules in order for the reader to have a basic understanding of the underlying methods, along with the dependencies among modules and dependencies on models and training data. Both Figure  1  and Table  1  provide an overview of these details, which together, describe the different categories of parameters, the object type, the CU8 and non-CU8 input data, the dependencies, the models and training data that are used, the approximate number of sources in Gaia DR3 and their G magnitude range for which a result can be found, see also Fig.
Total Galactic Extinction The Total Galactic Extinction module, TGE,  (Section 11. 3. 11, Delchambre 2022) , uses a subset of giants with extinction estimates provided by GSP-Phot as extinction tracers, to construct all-sky maps at various resolutions of the total foreground extinction from the Milky Way. The maps specify the median extinction A 0 of the tracers per HEALPix, where A 0 is the extinction parameter of the adopted extinction curve of  Fitzpatrick (1999) , Fig. 9. Examples of BP/RP simulations of different type of sources. All sources have been simulated at G=15. Red and black lines show spectra from MARCS library, having T eff = 3500 and 6000 K. In blue an OB spectrum with T eff =30000 K, in purple a WDA spectrum having T eff =15000 K and log g =8. 0. In orange and green, an SDSS QSO and an SDSS Galaxy, having redshift z=2.
Stellar evolution models Stellar evolution models are used in two of the Apsis modules, GSP-Phot and FLAME. For GSP-Phot the published APs are astrophysically self-consistent within the PARSEC 1. 2S Colibri S37 models  (Tang et al. 2014; Chen et al. 2015; Pastorelli et al. 2020, and references therein). Imposing these isochrones ensures that GSP-Phot can simultaneously fit the observed apparent magnitude (using the absolute magnitude) and the amplitude of low-resolution BP/RP spectra (using the radius, see Andrae 2022). Moreover, the isochrones ensure that only astrophysically reasonable parameter combinations are possible. For FLAME the mass, age and evolutionary stage are based on the use of the BASTI stellar models  (Hidalgo et al. 2018). In FLAME these models cover the zero-age main sequence until the tip of the red-giant branch, corresponding to evolutionary indices of between 100 and 1300 (main sequence < 390; turn-off = 390; sub-giant: 420 to 490, and giant > 490), and masses between 0.
Empirical training One of the drawbacks of training machine learning algorithms on synthetic data is that good results require (a) adequate source models from which to generate the synthetic data, (b) sufficient coverage of the parameter space by the source models, and (c) a good match between the synthetic data (Gaia simulations) and the real Gaia data of the corresponding objects. For five Apsis modules, specifically DSC, MSC, UGC, ESP-UCD, and ESP-ELS, one or more of these conditions could not be achieved, so for these we use empirical training. This involves training the algorithm on real Gaia data, with classes or astrophysical parameters for the training data obtained from external sources. Typically this involves cross-matching Gaia to external catalogues, such as the Sloan Digital Sky Survey (SDSS), and using class labels or APs obtained by others, e.
65, we also processed a pre-defined list of around 50 million sources with G > 17. 65. These limits in magnitude ensured roughly the same number of objects in each magnitude bin (∼130 million) and enabled the schedule to be optimized. For GSP-Spec we imposed a minimum SNR = 20 in the RVS spectra. This information is also provided in Table  1. For UGC, QSOC, and OA no limit was necessary because they process relatively few sources. DSC also had no limitations imposed because it was designed to run fast in order to process all 2 billion 12 sources in Gaia. The TGE module is very quick as it works on a HEALPix basis, but as it processes sources from GSP-Phot, no sources with G > 19 were included. In Fig. 10  11 https://gaia-dpci. github. io/GaiaXPy-website/ 12 The word billion implies 1 000 million.
CU8 data tables in Gaia DR3 The names and dimensions of the tables with CU8 parameters are summarised in Table  3. The first four tables contain APs for which processing was done on a source-by-source basis, such as T eff or redshifts. Most of the stellar parameters, classifications, individual extinction measurements, and auxiliary data are found in the astrophysical_parameters and astrophysical_parameters_supp tables, which contain only CU8 products. The former table contains one main result from each of the Apsis modules, while the latter table provides supplementary results in the form of specific libraries (GSP-Phot), methods (GSP-Spec), or input source types (FLAME). Some of the parameters from DSC and GSP-Phot from the astrophysical_parameters are copied to gaia_source for convenience to the user.
Parameters and fields In the ten archive tables (excluding gaia_source), there are a total of 538 fields produced by CU8 (excluding solution_id and source_id) 13. Each field has a field name associated with it, along with a data type, unit, and a simple and detailed description. Some of these field names are related. For example, for the chromospheric activity index there are three related activityindex fields, its value, uncertainty and information pertaining to the input data. For the stellar mass there is an upper and lower confidence level associated with the median value (three fields). Also, the mass was derived using two different sets of input data, to give a total of six mass fields. There are also some parameters that are produced by more than one Apsis module, such as class probabilities (classprob), T eff (teff), and [M/H] (mh).
and by GSP-Spec based on the analysis of the λ862 nm diffuse interstellar band (DIB). The TGE module exploits individual source-based extinction from GSP-Phot to provide a 2D total Galactic extinction map. Both GSP-Phot and MSC additionally estimate distances. Further details on most of these parameters is found in Fouesneau (2022), while TGE is discussed in Delchambre (2022).
Therefore, a value of v sin i (vsini_esphs) is provided along with the spectroscopic parameters for the brighter targets (where RVS spectra was available for processing). The mode adopted to process the data is stored in the first digit reading from the left of the ESP-HS flag flags_esphs. Its value is 0 for BP/RP+RVS processing and 1 for BP/RP-only processing. We show in Fig. 19  the Kiel diagram obtained in both modes. In the fainter magnitude regime (i. e. BP/RP-only mode), the overdensity perpendicular to the main sequence is mainly due to hot horizontal branch stars as was confirmed by a systematic query in the Simbad database  (Wenger et al. 2000).