dataesr/scientific-paragraphs-categorization huggingface dataset:
The 'WEAVE observation queue scheduler' ( :  Fariña et al. 2018 ) is bespoke software written for use by the WEAVE operator to make informed decisions about which OBs in the database are most suitable for observation, at least one hour ahead of time, given the typical observation length (which in turn is driven by the configuration time of a MOS field). Requirements set by the OBs on instrument configuration (i. e. observing mode) and observing conditions (e. g. sky brightness, seeing, etc. ) are combined with current and predicted weather conditions and current instrument configuration to allocate scores to OBs in the OB database. For example, poor seeing conditions might trigger a LIFU observation, during which no configuration is possible on either field plate. If this occurs during the night, the most efficient subsequent observation, once the seeing conditions start improving, is a mIFU field using plate B, as configuring a mIFU field takes less time (under 20 minutes) compared with a MOS field (just under one hour), during which plate A can be set up for a MOS observation.
On-Island Survey Management Team (OISMT) The On-Island Survey Management Team (OISMT) at the ING is responsible for many aspects of the running of the WEAVE facility, ranging from the commissioning of the WEAVE instrument to ensuring the day-to-day running of WEAVE in the contexts of both the WEAVE Survey and PI programmes 23  once the instrument is on sky. With support from the OISMT, a dedicated WEAVE instrument specialist will be responsible for managing the execution of nightly OBs, aided by the observation queue created by the based on current and predicted weather conditions and the observing requirements of OBs in the OB database, held at Observatory Control System. Regular communication between the OISMT and the Survey Working Group is key for ensuring the smooth and optimised scheduling of OBs from the WEAVE Survey and PI programmes combined, and this interface has been tested extensively during the most recent Operational Rehearsal (see Section 6).
2018; Myeong et al. 2019; Gallart et al. 2019; Naidu et al. 2020; Malhan et al. 2022). A number of new cold streams have also been discovered thanks to Gaia astrometry (e. g. Malhan et al. 2018; Ibata et al. 2021) , which anticipates the wealth of discoveries awaiting us with the combination of Gaia and WEAVE. While the main target of this survey is the Galactic halo, the survey will also probe the thick disc of the Milky Way, complementing the HR survey (see section below). To achieve these goals, the LR-highlat wide-area survey will target the following: colour-and magnitude-selected mainsequence turnoff (MSTO) stars; red-giant-branch (RGB) stars selected by colour, magnitude and Gaia astrometric data (parallaxes and proper motions, to reject local red main-sequence stars from the selection); extremely metal-poor star candidates selected from narrowband photometry (e.
Brown et al. 2018; Contigiani et al. 2019; Evans et al. 2022; Marchetti et al. 2022 ). This survey is foreseen to share fibres on the sky with the WEAVE-LOFAR and WEAVE-QSO surveys (see Sections 4. 7 and 4. 8, respectively), and hence share the same footprint. In addition to this wide-area component, the LR-highlat survey will drill a few deeper (down to at least  SDSS = 21) four-hour pointings (and even up to seven hours in some specific cases) around specific objects of interest, namely dwarf spheroidal galaxies and known stellar streams, where the multi-epoch data will allow for the detection of binary stars to refine the line-of-sight velocity dispersion profiles of these objects and to understand better the dark-matter distribution (e. g. Posti & Helmi 2019; Cautun et al. 2020). This pointed survey is expected to cover a total of ∼ 200 deg 2 on the sky.
This will allow us to combine the results of the GA sub-surveys both among themselves and with several other stellar Galactic surveys including Gaia-ESO  (Gilmore et al. 2012) , GALAH  (Buder et al. 2021) , APOGEE  (Majewski et al. 2017 ), 4MOST (de Jong et al. 2019)  and others; see e. g. Soubiran et al. (2022)  for a comparison of metallicity determinations for FGK stars across public data releases from several such spectroscopic surveys. In addition to dedicated calibration fields, it is highly desirable that a significant overlap is built between the high and low spectral resolution (HR and LR, respectively) Galactic Archaeology surveys in order to ensure homogeneity of the stellar parameters and individual chemical abundances that are derived between these two instrumental modes. While calibration stars (cluster and reference field stars observed specifically with both modes) provide the absolute anchor to these quantities through comparisons with independent estimates, it is desirable to build an overlap of stars surveyed in both LR and HR that would enable us to better map the systematics between stellar parameters and abundances derived from these two types of WEAVE spectra.
2017) , and fundamental nuclear reaction rates  (Kunz et al. 2002) , with important implications for stellar population synthesis and galaxy evolution theory  (Maraston 1998; Kalirai et al. 2014 ). Because of their well-constrained cooling ages, white dwarfs provide an insight into the age of the Galactic disc  (Winget et al. 1987; Oswalt et al. 1996) , open clusters (García-Berro et al. 2010) and globular clusters  (Hansen et al. 2007) , and can even trace variations in the Galactic star-formation rate  (Tremblay et al. 2014). White dwarfs will be observed as flux calibrators in WEAVE Survey observations, and the resulting spectra will provide a rich database for white-dwarf-enabled science.
Because of their small size, white dwarfs are intrinsically faint, and difficult to distinguish from more distant main-sequence stars with similar colours. Consequently, most known white dwarfs that were identified as ultraviolet-excess objects  (Green et al. 1986; Liebert et al. 2005; Kleinman et al. 2013 ) are young ( 1 Gyr) and hot ( eff 10, 000 K), and are utterly unrepresentative of the Galactic white dwarf population as a whole. Gaia DR2 finally provided the information necessary to break the degeneracy between nearby white dwarfs and background main-sequence stars: accurate parallaxes. Gentile  Fusillo et al. (2019)  assembled the first unbiased all-sky magnitude-limited ( 20) sample of 260 000 white dwarf candidates using Gaia DR2, followed by an updated catalogue using Gaia EDR3 data in Gentile  Fusillo et al.
(2021). While the Gaia data are sufficient for identifying white dwarfs with high confidence, follow-up spectroscopy is required (Figure  11 ) to determine their physical properties (effective temperature, surface gravity, atmospheric composition, magnetic field strength, multiplicity) and derive fundamental properties (mass, cooling age, progenitor mass) that are necessary to address the science areas outlined above. WEAVE will target 100 000 white dwarfs with  ≤ 20 (Figure  12 ) for LR spectroscopy, and will roughly triple the number of white dwarfs with high-quality spectroscopy in the Northern hemisphere  (Kleinman et al. 2013; Gentile Fusillo et al. 2015). The WEAVE White Dwarfs survey will establish the first large and homogeneous spectroscopic sample that is not subject to complex selection effects, and is therefore ideally suited for detailed statistical analyses of white dwarfs in the context of galactic, stellar, and planetary structure and evolution.
The data will also make possible the detection of kinematic twists, warps and decoupled components. The WEAVE-Apertif survey will also deliver spatially resolved stellar age distributions, ionised-gas metallicities and star-formation rates that, together with the H data, will provide a true legacy survey. As the effects of the environment on the general properties of galaxies will have been revealed by the LR dataset, a detailed study of a subsample of galaxies using the high-resolution capabilities of the LIFU will help to establish the effects of H accretion history in the secular (i. e. internal) evolution of spiral-galaxy discs (see for a review van Gorkom 2013). The impact of the H accretion history on the chemo-dynamical properties of galaxies can be quantified by analysing the stellar kinematics and stellar properties of a sample of galaxies with perturbed H morphologies, and comparing them to those of galaxies with no signs of perturbed H morphologies.
The success of the project and its legacy impact rely on obtaining quality data reaching   = 24 mag arcsec -2 at ∼ 3. Finally, these datasets will enable us to constrain the disc mass of the full sample of 400 Apertif galaxies, allowing us to relate dynamical mass to the environment. The dark-matter content in both HSB and LSB galaxies can be constrained by combining kinematic data from both H and optical data and applying the traditional technique of rotation-curve decomposition as well as other dynamical modelling techniques. However, the actual total mass residing within a galaxy's disc remains a controversial matter. This is an important calculation to get right, as the mass-to-light ratio of a disc determines the disc's contribution to the galaxy's rotation curve, which in turn constrains the density and scale-length of the halo.
These large numbers of sources will lend immense statistical power to this uniformly selected and homogeneous spectroscopic data bank, offering us the chance to simultaneously model the populations of SFGs and AGN as a function of stellar mass, environment and redshift. In this way, we can shed new light on the complicated interplay between the key processes thought to shape galaxy formation and evolution. WEAVE-LOFAR spectroscopic redshifts will also enable much improved cosmological parameter determination from LoTSS, building on the earlier work by  Siewert et al. (2020) ,  Alonso et al. (2021)  and  Tiwari et al. (2022). To achieve these goals, WEAVE-LOFAR has been designed to predominantly utilise WEAVE's MOS mode, using a tiered layout. The tiers -chosen to statistically sample the redshift-luminosity plane, the changing demographics of the radio source population, and the processes driving their evolution -are as follows: • Wide tier, targeting sources brighter than ∼10 mJy at 150 MHz selected over an area of up to 10 000 deg 2 from LoTSS; • Mid tier, targeting 150 MHz sources brighter than ∼ 1 mJy, including over the HETDEX spring field (Figure  20 ; where more than 400 deg 2 of the LoTSS first data release are located) and targeting fainter 150 MHz sources > 500 µJy over the unique Herschel-ATLAS NGP field; • Deep tier, covering an area of up to 50 deg 2 and targeting all radio-detected sources detected in the LoTSS Deep fields data covering the Boötes, Lockman Hole, ELAIS-N1 and NEP fields.
3) and 'sky' positions if the user chooses to specify these in advance, form the input to (see Section 2. 2), the output of which are configured (or ' -output') XML files; -for the mIFU mode, protofields are similar to those created for the MOS mode but contain only positions of the objects contained within the boundaries of (and typically centred on) the 20 mIFU 36 http://iraf. noao. edu/specatlas/thar/thar. html bundles, which translates into a set of fibre positions for the individual mIFU fibres of each mIFU bundle; -for the LIFU mode, a protofield contains a single pointing that defines the centre of the field (along with a position angle set to optimise the guide star position on the offset LIFU guider), which translates into a set of fibre positions for the fixed LIFU fibre array; • XML files are submitted to the WASP, which performs a series of checks on the files received, ranging from a baseline structure check against template XMLs for the checking of constituent elements, to the verification of instrumental mode and observational requirements against the CNAME-containing FITS catalogues from an earlier stage of the process; • WASP-verified XML files are collated and sent to the OCS, which then ingests the files as OBs into its OB database; • prior to and during each (simulated) observing night, the -(WEAVE observation queue scheduler; see Section 2.
The resulting datasets were then made available to the QAG and SWG for analysis. A key aspect of this process is that every step in the data flow should occur automatically, driven only by the observation description and calibration templates in the OB, and by information generated internally within CPS and APS, which have no prior knowledge of the input target parameters beyond the basic photometric data. Flux calibrations were generated from the white dwarf calibration stars within each field, and the sky and object fibres were subject to the variations in throughput arising from the non-telecentricity and vignetting in the optical models. Figures 24-27 provide a few illustrative results from OpR3b (see also Figure  17 ). As the first end-to-end test of the complete CPS and APS pipelines, it was not expected that we would meet our science goals in the output of these systems in OpR3b.
• OpR3c allowed science teams to identify which other surveys typically compete for similar observing conditions, modes, and sky positions. This in turn helped us identify new functionalities to be implemented in the , allowing for a better sampling of OBs from the OB database, thereby creating both a better balance of OBs observed across the different surveys as well as better OB selection within each survey according to its science goals. OpR3c further identified some collisions between surveys whose science requirements call for similar observing conditions, which have now been ameliorated through adjustments made to the relative, intersurvey observation-time allocations in the survey planning. • The combined effect of the requests in right-ascension distribution, observing conditions and instrumental setup by community PI programmes cannot be predicted in advance, and neither, therefore, can the effect that this will have on their interplay with the WEAVE surveys in the OB scheduling process.
• OBs should have expiration dates so that the OB database at the OCS (in general) contains only currently observable OBs, and to allow surveys to refresh the content of the OB database in case of changes in their plans. An expiration date of 9 months after ingestion is now set for any OB entering the database. An exception to this rule is if there are OBs in the database belonging to an already started 'chain' (i. e. duplicate OBs to enable a longer total exposure of the field), in which case the remaining OBs have their expiration dates removed. • There is a need for the ability to retract OBs (prior to them being observed) or data (at different stages of the data-processing and archiving process). Implementation of the necessary protocols for this within the SPA is currently in progress.
Acknowledgements We would like to thank the reviewers of the  WEAVE Survey Strategy Review held at Observatoire de Paris  (2015 October [19][20],  M. Bershady ,  G. Hill ,  F. Najarro , and  R. Schiavon , for their constructive and critical comments on the science and survey cases presented then, and which formed the basis of the cases presented here as well as the subsequent, extensive  WEAVE Science Case  and  Survey Plan , which were reviewed at the  WEAVE Survey Readiness Review held  at the  University of Oxford  ( 2020 January 15-16 ) by  G. Hill ,  S. Martell ,  K. Master  and  A. McConnachie. We are also very grateful to these reviewers for their constructive and supportive critique. S. C. T. and G. D. thank the  WEAVE Project Board and ING Board  for their strong support for the project and many helpful discussions.
Metabolic network reconstruction The metabolic reconstruction of F. succinogenes S85 was performed using the freely available workspace AuReMe (automatic reconstruction of metabolic models)  (38). AuReMe embeds existing tools as well as ad hoc packages to reconstruct and handle GEMs. It uses (i) the outputs of the Pathway Tools software  (39, 40)  to perform annotation-based reconstruction, (ii) the OrthoFinder method  (41, 42)  to perform orthologybased reconstructions, and (iii) the Meneco tool  (43)  to perform a gap-filling procedure. AuReMe relies on the PADMet library (Python library for handling metadata of metabolism  [38] ) to ensure the reproducibility of the workflow used to create a GEM, to guaranty the interoperability between the different methods used, to curate GEMs, and to export the GEM under several formats (SBML, matrix, wiki, RDF-like format).
Step 1 (collecting genomes and reference reaction data set) The two complete annotated genomes of F. succinogenes S85 were downloaded, respectively, from https://www. ncbi. nlm. nih. gov/nuccore/CP001792. 1 and https:// www. ncbi. nlm. nih. gov/nuccore/CP002158. 1. Each F. succinogenes S85 genome contains 3. 84 Mbp with, respectively, 3,160 and 3,174 genes identified. The biomass reaction of Escherichia coli K-12 MG1655 (45) was adapted to F. succinogenes and used to build the metabolic network (Table  S1  in supplementary material A; the supplemental material is available at https://doi. org/10. 5281/zenodo. 7228115). The list of seeds (essential constituents of the culture medium to guarantee growth) was prepared based on the minimal medium composition needed for F. succinogenes growth (Table  S2 ).
Step 2 (generating draft models) A first GEM was reconstructed according to the genome annotations via Pathway Tools using both complete genomes: NC_013410. 1 and NC_017448. In parallel, orthologybased reconstruction GEMs were obtained using the GEMs of the following gut microbes: Bacteroides thetaiotaomicron iAH991 (  46 ), E. coli K-12 MG1655 (45), Faecalibacterium prausnitzii A165 (29), Bifidobacterium adolescentis L2-32  (28) , and Lactobacillus plantarum WCFS1  (47) , and mapped to MetaCyc  (48) , thanks to the MetaNetX database  (49). Finally, all GEMs obtained from the annotation and the orthology reconstruction steps were combined into a draft GEM with the PADMet library.
Step 3 (gap filling) To analyze and curate the GEM of F. succinogenes S85, we applied a gap-filling procedure. Here, a GEM is considered as a graph in which metabolites are nodes and reactions are the links between the nodes. In these analyses, stoichiometry is not considered. This procedure allows adding reactions to guarantee the production of specific metabolites according to a graph-expansion criterion. We used Meneco (metabolic network completion  [43] ) and the MeneTools package (metabolic network topological tools  [38, 50] ) for this gap-filling step.
Step 4 (manual curation and gene-reaction association) The draft network was manually curated to find potential errors and filling gaps based on the phenotype and experimental data reported in the literature. FBA was used to reconstruct and validate models for maximizing the biomass reaction flux. Some manually and gap-filled added reactions had no gene associated. All the gene sequences from other bacteria associated with these reactions were identified in the National Center for Biotechnology Information (NCBI) using the reaction Enzyme Commission (EC) number. The corresponding protein sequences were aligned using BLAST (basic local alignment search tool  [51] ) to the F. succinogenes S85 translated genomes. The identified proteins with identity >76% with coverage throughout the sequence were associated with their corresponding reactions in the GEM.
Differently from other methods, NetRed performs the reduction of the stoichiometric matrix of the full network through matrix algebra based on given flux vectors and a list of protected metabolites  (57). This method provides a reduced network and its correspondent flux vector, which is consistent with the flux vector of the full stoichiometric matrix. NetRed is implemented in the MATLAB COBRA toolbox  (59). This is advantageous since the results from FBA, also implemented in the COBRA toolbox, can be directly used for the reduction. As NetRed can manage several flux vectors, the reduced network can represent various flux distributions and provide a unique biomass reaction that respects the given flux vectors. Additionally, the use of the matrix approach allows mapping the reactions in the reduced network into their corresponding ones in the full network.
Parameter identification The model parameters were estimated from the in vitro experimental data detailed in the sections Culture conditions and sample preparation and Quantification of substrate consumption and metabolite production, where F. succinogenes was grown on three different substrates (glucose, cellobiose, and cellulose). During culture on these substrates, growth (optical density [OD] 600 nm), production of metabolites (succinate, acetate, and formate), ammonia and carbohydrate consumption were monitored at six time points. The dynamic concentration of the metabolites were used in the model calibration routine using the maximum likelihood approach implemented in the MATLAB toolbox IDEAS  (60) , which is freely available at http://genome. jouy. inra. fr/logiciels/IDEAS. The optimization uses the quasi-newton algorithm implemented in the MATLAB function fminunc.
FBA for unblocking biomass and manual curation In our reconstruction process, the aim was to obtain a functional high-quality network, which produces biomass yield. For this purpose, we first focused on reaching topologically all the targets according to the qualitative network-expansion criteria. Forty-one reactions were expertly added to the gap-filled model network. These reactions belong to four distinct databases: MetaCyc  (48) , KEGG  (67) , BiGG  (68) , and RHEA  (69). Afterward, we manually unblocked all the pathways referring to FBA analysis, which were in direct and indirect relationships with each component of the biomass reaction. We therefore focused on curating the network and the pathways involved in carbon and nitrogen metabolism as well as some essential cofactor biosynthesis.
FBA and essential reactions The obtained network has 1,317 genes, has 931 pathways, and is composed of 1,565 reactions, from which 1,211 are associated with genes (Table  1 ). The final network contains 1,586 unique metabolites and is available online as a wiki page at: https://gemaureme. genouest. org/fsucgem/index. php/Fsucgem. We investigate our final metabolic network using FVA. All 85 target components are reached topologically, 38. 5% of the reactions are active, of which 137 are essential reactions for biomass production. The simulated growth rate from F. succinogenes S85 metabolic model is 0. 137/hour. Three hundred eighteen out of 931 (34%) metabolic pathways are complete at more than 75% of the reactions present in the KEGG and MetaCyc databases. 76% of those active pathways have at least one reaction with flux according to the FBA analysis, and 150 pathways are 100% active in flow (Data set S2 in supplementary material B).
Glycogen biosynthesis and degradation pathways As an example to illustrate the use of the reconstructed network, we analyzed the glycogen biosynthesis and degradation pathways, because glycogen has been shown to be simultaneously synthesized and degraded in F. succinogenes during all growth phases  (19). Intracellular glycogen accumulation is carried out by the consecutive action of ADP-glucose pyrophosphorylase (glgC, FISUC_RS14455 FSU_RS00645) (EC 2. 7. 7. 27), glycogen synthase (glgA FSU_RS16140 FISUC_RS15965) (EC 2. 4. 1. 21), and glycogen-branching enzyme (glgB FISUC_RS15575 FSU_RS01770) (EC 2. 4. 1. 18) (Fig. 4 ). All these anabolism reactions and genes were identified by annotation, except the phosphoglucomutase (pgm FSU_0773) that was linked to the reaction EC 5. 4. 2. 2 by manual curation (Data set S1 in supplementary material B).