dataesr/scientific-paragraphs-categorization huggingface dataset:
Quality and format checks The first step of the data analysis is to perform sanity checks on the files provided to WG15 from the four analysis WGs. A first pass is performed by a dedicated automated tool (the FITSCHECKER which was developed and maintained over the lifetime of the survey by C. Worley, A. Casey, D. Murphy, and A. Gonneau). This tool flags issues in the file formats, data statistics, and data completeness in a report that is sent to the submitter. The submitter must resolve any issues and resubmit the file until it is accepted by the tool as adhering to the GES data model. The GES data model is described in two technical documents governing the spectral formats from the spectral reduction pipelines 5  and the analysis results catalogues from the nodes and WGs 6. WG15 members also carried out a visual inspection of the data statistics summarised in the FITSCHECKER report to identify any remaining spurious values and outliers.
With the relevant sets of node files, the WG leads performed their own internal homogenisation of the parameters in the parameter phase and the abundances in the abundance phase to provide a set of recommended results for each CNAME specific to the WG datasets. At the end of each phase, WG15 performed a cross-WG homogenisation to produce the final recommended set of parameters for the parameter phase, and then abundances for the abundance phase. The homogenised WG14 flags are also included (see  Van Eck et al. , in prep. , for details on the analysis of each WG). A first-pass analysis of the flags becomes available as a reference set during the parameter homogenisation. The radial velocities (RVs) are homogenised by WG15 in parallel using stars analysed in common across different instrumental setups for calibration, as is done for parameter and abundance homogenisation.
Asteroseismic calibrations The agreement with the log g of the asteroseismic samples of CoRoT and K2 as described was investigated by WG15. For WG10, 1512 CNAMEs had both CoRoT and A129, page 13 of 33 GES log g, while 28 CNAMEs had both K2 and GES log g. For WG11, 86 CNAMEs had both CoRoT and GES log g, while 62 CNAMEs had both K2 and GES log g. The differences between these values and the seismic log g values are shown in Fig. 15 , and the median difference and standard deviation in each set are provided. In three of the four datasets, there is good agreement between the seismic and GES log g values. However, the WG10 log g values of the K2 sample are overestimated by 0. 28±12 dex. While the agreement between the WG10 values and CoRoT is good, there is large scatter (-0. 07 ± 0. 51). The nature of the WG10 spectra, which span a shorter wavelength range and have a lower resolution than the WG11 spectra most likely contributes to both effects as noted in  Worley et al.
(2015). The 12 C 14 N molecular bands 6470-6490 Å, the C 2 Swan (1,0) band head at 5135 Å, the C 2 Swan (0,1) band head at 5635. 5 Å, and the forbidden [O I] line at 6300. 31 Å are used. The analyses are performed through spectral synthesis with the Turbospectrum code  (Plez 2012). For the determination of the oxygen abundance, the oscillator strengths of the two lines of Ni are taken into account  (Johansson et al. 2003). The carbon abundance is also derived from atomic lines (C I). In this case, the abundance is derived by several nodes, and homogenised with the same procedure as for the other elements. In the final database, the abundances of C and N from molecular bands are indicated with C_C2 and C_CN.
WG15 additional flags Additional rules are added at the WG15 level depending on the REC_WG provenance previously assigned to each CNAME. This new set of rules is designed to add WG15 flags or in some cases to remove stellar parameters. The detailed flow chart is shown in Fig. 33. 'Parameters' here refers to the columns TEFF, LOGG, FEH, XI, MH, and ALPHA_FE and all associated number and error columns in the final database. We note that the flag suffixes do change between WGs, and so they are not all identical, even though they may appear to be at first sight. If multiple flags are activated, the WG15 flags are concatenated using '|'. The WG15 flags are then concatenated with the existing TECH column. Notes. Here, we provide the offset, standard deviation, and number of stars in common for the bootstrap to the initial setup (Y), and for the overall bootstrap (BS) to HR10.
Simplified flags The TECH flags cover a broad range of topics (S/N, data reduction, determination, and quality of stellar parameters/abundances). The syntax of the flags allows us to quickly identify the issue (prefix), trace the working group (WG ID) and node (node ID) from which it originates, and, in some cases, obtain extra information (suffix). However, this system is too detailed for the end users who want to quickly use the Gaia-ESO data. For iDR6, a system of simplified flags has therefore been designed for the Gaia-ESO Survey. These simplified flags must allow the end users to quickly filter the data in order to meet their science goals; they should allow the user to quickly reject objects with non-physical or highly suspicious results, and they complement the information already carried by the error bars meaning of each flag are listed in the table below.
Comparison with GALAH and APOGEE surveys Figure  36  shows comparisons of GES data to both GALAH data and APOGEE data. We selected the GES stars that are in common with each survey. The plots show typical comparisons for the α-elements, Mg and Ca, an iron-peak element, Cr, and a neutron-capture element, Ce. It may also be seen that the dispersion found in the GES results is generally smaller than that found in the results of the other surveys; the results for the element Cr provide a good example of this. Table  4  presents the abundance [X/Fe] median differences and the associated dispersion between GALAH, APOGEE, and GES WG15 for the sample of stars in common. In most cases, the median difference is below 0. 1 dex, demonstrating the excellent agreement between the surveys.
The latter contains observations with HR10, HR15N, HR10:HR21, HR21, and HR9B. The median differences for both samples are close to zero, with a dispersion of 0. 16 dex, indicating, on average, very good agreement in terms of the A129, page 28 of 33  Hourihane, A. , et al. : A&A proofs,. Comparison between WG15, GALAH, and APOGEE abundance ratios for the sample of stars in common. WG15 data are represented as blue circles. Red symbols represent APOGEE data and black symbols are the data from the GALAH survey. accuracy of Gaia compared to GES, and, as expected, a lower precision for Gaia compared to GES.
The numerous figures of this article give a good overview of the quality of the homogenisation process and of the final abundance results compared to literature data and large spectroscopic survey results. Many of the surveys currently underway and in preparation have been inspired by the Gaia-ESO structure and approach. For example, the FITS format for data exchange is currently adopted by most Galactic spectroscopic surveys, which was not previously common in this field; the Gaia-ESO line list is widely used, and the use of the various categories of calibrators has been widely adopted, particularly that of star clusters. The experiment with multiple pipelines was helpful in understanding the inherent limitations of spectral analysis and in finding the origin of systematic errors in the various approaches.
Acknowledgements. Based on data products from observations made with  ESO Telescopes  at the  La Silla Paranal Observatory  under programmes 188. B-3002 ,  193. B-0936 , and  197. B-1074. These data products have been processed by the  Cambridge Astronomy Survey Unit (CASU) at the Institute of Astronomy, University of Cambridge , and by the  FLAMES/UVES  reduction team at  INAF/Osservatorio Astrofisico di Arcetri. These data have been obtained from the  Gaia-ESO Survey Data Archive , prepared and hosted by the  Wide Field Astronomy Unit, Institute for Astronomy, University of Edinburgh , which is funded by the  UK Science and Technology Facilities Council. This work was partly supported by the  European Union   FP7 programme  through  ERC  grant number  320360  and by the  Leverhulme Trust  through grant  RPG-2012-541.
823734. D. K. Feuillet  was partly supported by grant no. 2016-03412  from the  Swedish Research Council. D. Montes  acknowledges financial support from the  Agencia Estatal de Investigación of the Ministerio de Ciencia, Innovación  through project  PID2019-109522GB-C54 /AEI/10. 13039/501100011033. E. Marfil acknowledges financial support from the  European Regional Development Fund (ERDF)  and the  Gobierno de Canarias  through project  ProID2021010128. J. I. González Hernández  acknowledges financial support from the  Spanish Ministry of Science and Innovation (MICINN)  project  PID2020-117493GB-I00. M. Bergemann  is supported through the  Lise Meitner  grant from the  Max Planck Society  and acknowledges support by the  Collaborative Research  centre  SFB 881  (projects  A5 ,  A10 ),  Heidelberg University , of the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation).
This project has received funding from the  European Research Council (ERC)  under the  European Union‚ Horizon 2020 research and innovation programme  (Grant agreement No. 949173 ). P. Jofré  acknowledges financial support of  FONDECYT Regular   1200703  as well as  Núcleo Milenio ERIS   NCN2021_017. R. Smiljanic  acknowledges support from the  National Science Centre, Poland  ( 2014/15/B/ST /03981 ). S. R. Berlanas  acknowledges support by  MCIN/AEI/10. 13039/501100011033  (contract  FJC 2020-045785-I ) and  NextGeneration EU/PRTR and MIU  ( UNI/551/2021 ) through grant  Margarita Salas-ULL. T. Bensby  acknowledges financial support by grant No. 2018-04857  from the  Swedish Research Council. T. Merle  is supported by a grant from the  Foundation ULB. T. Morel  are grateful to  Belgian F.
R. S. - FNRS  for support, and are also indebted for an  ESA /PRODEX Belspo contract related to the Gaia Data A129, page 30 of 33 Hourihane, A. , et al. : A&A proofs, manuscript no. aa45910-23 Processing and Analysis Consortium and for support through an  ARC  grant for Concerted Research Actions financed by the  Federation Wallonie-Brussels. W. Santos acknowledges  FAPERJ  for a Ph. D. fellowship. H. M. Tabernero  acknowledges financial support from the  Agencia Estatal de Investigación of the Ministerio de Ciencia, Innovación  through project  PID2019-109522GB-C51 /AEI/10. 13039/501100011033.
The prospective INSPIRIS RESILIA Durability Registry (INDURE) aims to provide data on short-term clinical effectiveness, as well as on long-term hemodynamic and structural performance in patients <60 years. Here, we report 1-year data of patients enrolled. INDURE is a prospective, open-label, multicentre, international registry to assess the clinical outcomes of patients younger than 60 years of age who undergo SAVR with the INSPIRIS RESILIA aortic valve  [13]. Patients were enrolled at 21 sites across Austria, Belgium, France, Germany, Italy, the Netherlands, Spain, UK, and Canada.
Statistical analysis Data were analyzed using descriptive statistics, with categorical variables presented as absolute values and frequencies (%) and the continuous variables presented as means (standard deviation  [SD] ) and/or median (interquartile range [IQR]). Test for normal distribution was carried using Kolmogorov-Smirnov-test. Wilcoxon signed ranks test for paired data was used for comparing QoL scores between baseline and follow-up visits. For outcome reporting Kaplan-Meier estimates were provided. A p-value of <0. 05 was considered statistically significant. Statistical analysis was performed using SPSS Version 28. 0 (Armonk, NY, IBM Corp. )  [16].
RESULTS A total of 457 patients were enrolled between April 2019 and May 2021. For the present analysis, 36 patients with a Bentall procedure and mitral/pulmonary valve replacement were excluded, resulting Of the remaining 404 patients, 7 (1. 7%) patients died, which resulted in a total of 397 (94. 3%) patients alive with available data at 1 year (Figure  1 ).
g. Halttunen et al. , 2018; Lennox et al. , 2023). The timing of the fate event (e. g. predation) is important to assess, to separate tagged animal behaviour from other data in the subsequent analyses. Methods for tagging have been extensively covered elsewhere and do not bear repeating here  (Brown et al. , 2011; Jepsen et al. , 2015) , except for specific notes relevant to positioning. External tags are likely to have a larger detection radius  (Dance et al. , 2016) , particularly for animals with thick abdominal walls that will more significantly attenuate the signal, but internal tagging should be considered if external transmitter attachment poses extra drag to the animal, increases the visibility of the tagged animal to predators, increases the potential for entanglement, or moves too freely around the animal and comlicates positioning.
thus occupy a finite space limited by detection range and number of receivers. Near real-time positioning faces additional limitations of data transmission rates between mobile receivers and a central computer. As ambitions for studying animals scale up to major lakes or marine areas, such innovations will greatly expand the applicability of positioning beyond what has been possible to date. Computing power needed to calculate positions is a clear limitation to scaling up positioning studies. Running synchronization models and positioning algorithms on these large datasets is time consuming, computationally expensive, and may be inaccessible to smaller research groups without access to servers or centralized computing. How to store, analyse, use and transform these massive amounts of movement data into useful knowledge is already a challenge for many acoustic positioning studies.
However, the collective experience, including successes and failures, of the scientific community involved in positioning aquatic animals now leaves us with the knowledge necessary to routinely plan and execute successful fine-scale tracking projects across a range of aquatic environments (from inland rivers and lakes to coastal marine waters) according to best practices, generating rich datasets of animal movement. Advances in computing power, battery life, clock synchronization techniques, positioning solvers, signal encoding methods (CDMA-variants) and array design knowledge have been key contributors towards reaching the high data yields currently achievable. Fish can now be positioned with remarkable accuracy and precision, provided that care is taken in the development of tracking systems and the interpretation of findings.
Downloaded from https://besjournals. onlinelibrary. wiley. com/doi/10. 1111/2041-210X. 14191 by Inrae -Dipso, Wiley Online Library on [05/09/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2041210x, 0, could improve the quality of positional telemetry data by allowing feedback in near real time. In most contemporary positioning stud- ies, positions are only derived after the conclusion of a study, pre- venting opportunities to identify and address performance issues as they arise. Synchronizing clocks on independent receivers is one of the most challenging steps to overcome in acoustic positioning. Cabled receivers eliminate this challenge but limit coverage area and im- pose significant logistical constraints, so independent receivers are more common.
Dose determination and dose confirmation studies Live flea counts of each time-point were used to calculate arithmetic means by study day and treatment group. Geometric means (count þ 1 data with 1 subsequently subtracted from result) were additionally calculated in dose determination and confirmation studies, but efficacy claims were based on arithmetic means. Adequacy of infestation was demonstrated in the negative control groups when at least six cats were infested with ! 50 live fleas (flea retention ! 50%) at each time-point. Efficacy (%) was calculated using the Abbottʼs formula: 100 Â (C-T)/C, where C is the arithmetic/geometric mean of live flea counts on cats in the negative control group and T is the arithmetic/geometric mean of live flea counts on cats in the treated groups  (Abbott, 1925).
Group comparisons were made using a one-way analysis of variance (ANOVA) in SAS 9. 3 (SAS Institute Inc. , Cary, NC, USA) with a treatment effect, assuming a normal distribution of the data. All hypotheses were tested at a two-sided 0. 05 level of significance. Efficacy was claimed when efficacy ! 95% was calculated and a statistically significant difference (P 0. 05) between the treatment group and control group was demonstrated. The experimental unit was the individual cat.
Speed of flea kill study Live flea counts were analysed with an ANOVA model in SAS 9. 4 (SAS Institute Inc. , Cary, NC, USA) including treatment as a fixed effect. Efficacy was calculated using least square means and the Abbottʼs formula as described in Section 2. 4. 1. All hypotheses were tested at a two-sided 0. 05 level of significance. Efficacy was claimed when efficacy ! 90% was calculated and a statistically significant difference (P 0. 05) between the treatment group and control group was demonstrated. The experimental unit was the individual cat.
Rebus scoring and analysis We scored rebus resolution (RR) by attributing 1 point if the rebus resolution was found among the 6 written associations upon the related target word, independently of the fact if the rebus word appeared as the first or as a later association. This gives us a binary by-observation score (0 or 1). For example, for the target word félin "felin" we counted if panthère "panther" occurred among the 6 associations to félin "felin" either in EX or CR of both FW and RV. We analyzed the RR score using a Generalized Linear Mixed Model computed by the GAMLj module  (Gallucci, 2019)  of Jamovi software (2022) (R Core Team, 2021). We used Generalized Linear Mixed Models to determine the impact of the EX and CR conditions and of the FW and RV variations on rebus resolution for each observation.
Participants A total of 550 participants completed the entire online survey hosted by Limesurvey (all were majors and recruited on social media platforms). 80 participants were eliminated: 34 because they already have participated to one of our pilot studies and 46 because they were not native French speakers or had insufficient self-assessment scores for French language proficiency (see section "2. 1. 1 Participants" for further details). Table  7  shows the demographic data for Experiment 2.
Acknowledgments GO's thesis started with 6 months grant from the  American Psychoanalytic Association (APsaA)  and continued with no funding for 5 years. We are heartfully thankful to the  APsaA ,  Mr. Robert Berry ,  Mrs. Aliza Shevrin , and the  International Psychoanalytic Association (IPA)  for their respective modest contributions which for us turned out to be vital. GO also wants to thank the members of her Ph. D. doctoral committee:  Stijn Vanheule  at the  University of Ghent  and  Regine Kolinsky  and  Nathalie Vecruysse  at the  Université libre de Bruxelles  and  Mr. Mathieu Frenot  for his support during the data-analysis. This research has been done with the help of many Master students and trainees at the  Université libre de Bruxelles  and a special mention goes to  Mario Bestion.
The excellent delensed spatial resolution down to ≈100 pc in rest-frame UV observations allow the examination of the spatial variations of the UV radiation. All these information yield unparalleled understanding of lowmass, young galaxies at high redshift using A2744-arc3 as a textbook example. This paper is organized as follows. In Section 2, we describe the observations and data reduction, including JWST/NIRISS imaging and spectroscopy, Hubble Space Telescope (HST) imaging, VLT/MUSE and ALMA continuum observations. In Section 3, we provide details of photometry, SED fitting, and the lensing reconstruction in source plane. We also show the spatially resolved UV slope map. In Section 4, we present further analysis on the rest-frame UV and optical spectrum of A2744-arc3, including the analysis of both emission and absorption features.
2020) ; the pixel scale of final mosaicked images was resampled to 0 03 with pixfrac = 0. 8. We reduced NIRISS grism data using GRIZLI,  17  and the 2D grism spectra were drizzled with a pixel scale 0 065. To optimally extract 1D spectra of A2744-arc3, we first split the entire arclet into six segments, each with one clump. We extracted their 1D spectra using fifth Chebyshev polynomials. We summed over five 1D spectra of the six segments to get the final spectra. One of them was excluded since it is severely contaminated by zeroth-and first-order images from other sources.
ALMA Continuum Observations The millimeter continuum observations are obtained from the ALMA Lensing Cluster Survey. A2744 is observed by ALMA band 6 (Program 2018. 1. 00035. L, PI: Kohno; 2013. 1. 00999. S, PI: Bauer), which covers a frequency range with two tunings of 250. 0-257. 5 GHz and 265. 0-272. 5 GHz, corresponding to wavelengths of ∼1. 15 mm  (Sun et al. 2022). The 1σ continuum sensitivity or say rms is 45 μJy beam -1. The data were reduced using the standard pipeline with the CASA  (McMullin et al. 2007 ) with natural weighting, and will be described in details in S. Fujimoto et al. (2022, in preparation). This leads to a synthesized beam size (FWHM) of ∼1″, corresponding to ∼6. 9 kpc at z ∼ 4.
We perform SED modeling with the Bayesian code BEAGLE  (Chevallard & Charlot 2016). We assume a constant star formation history, the dust extinction curve of the Small Magellanic Cloud with the optical depth in the V band varying in the range 0-0. 5, and the  Chabrier (2003)  initial mass function with an upper limit of 300M e. The metallicity of the interstellar medium is kept the same as that of the stellar populations. A flat prior is applied for metallicity, 0. 01 < Z/Z e < 0. 2, and ionization parameter (the dimensionless ratio of the number density of H-ionizing photons to that of hydrogen) in log space, -< <-U 3 log 1, for nebular emission  (Gutkin et al. 2016). These priors are chosen on the basis of detections of the UV emission lines (see Section 4. 2 for more details). A JWST NIRISS/F150W view of A2744-arc3, the SED fitting results, and the NIRISS grism 2D and 1D spectra are shown in Figure  1.
X. L. and Z. C. thank Eros Vanzella, Lilan Yang, Dandan Xu, and Shiwu Zhang for very helpful discussions. X. L. thanks Zechang Sun for the useful tool SpecViewer (Sun 2021). Z. C. , X. L. , Y. W. , Z. L. , M. L. , and S. Z. were supported by the National Key R&D Program of China (grant No. 2018YFA0404503), the National Science Foundation of China (grant No. 12073014), and the science research grants from the China Manned Space Project with No. CMS-CSST2021-A05. F. S. acknowledges support from the NRAO Student Observing Support (SOS) award SOSPA7-022. F. S. and E. E. acknowledge funding from JWST/NIRCam contract to the University of Arizona, NAS5-02105. J. X. P. acknowledges partial support from NSF AST-2107991. D. E. acknowledges support from the Beatriz Galindo senior fellowship (BG20/00224) from the Spanish Ministry of Science and Innovation, projects PID2020-114414GB-100 and PID2020-113689GB-I00 financed by MCIN/AEI/10.