dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This work has been supported by the  European Metrology Program for Innovation and Research (EMPIR)  within the joint research project  EMPIR 19ENV04   MAPP. The  EMPIR  is jointly funded by the  EMPIR  participating countries within EURAMET and the  European Union. Additionally, the activities have been developed within the framework of the  World Meteorological Organization (WMO) Commission for Instruments and Methods of Observations (CIMO) Izaña Testbed for Aerosols and Water Vapour Remote Sensing Instruments. AERONET sun photometers at  Izaña Observatory (IZO)  were calibrated through the  AEROSPAIN Central Facility.
Respective profiles are shown in figure 5  from CX recombination spectral lines of B 5+ ions assuming T i ≃ T B 5+ , which is justified by the energy equilibration time between impurities and main ions being much shorter than the local transport time scale  [23]. The value of p e can be used to estimate the intra-ELM plasma density and electron temperature at the target plate (see section 2. 4), which cannot be measured directly by the divertor Langmuir probes. Profiles of p e in the pedestal region are determined by an integrated data analysis approach, which derives the pressure profile as an optimal fit to the available temperature and density data  [24]. Corresponding results are shown in figure  5  for the two experiment scenarios, with p e at the pedestal top ≈4 kPa (figure  5(c )) for reversed and ≈6 kPa, (figure  5(d) ) for the default I p and B t configuration.
s (m) Bx (T) By (T) Bz (T) φ θ 1. The local electron flux, density and temperature are derived from Langmuir probe measurements  [27]. Due to the limited battery voltage of the probe diagnostic power supply, the divertor triple probes in ASDEX Upgrade cannot acquire the full ion saturation current during ELMs. Therefore, both j sat and T e are generally only available for the inter-ELM phases of H-mode discharges. Using the measured poloidal current into the outer target plate (figure  3 , row z) as an indicator for the ELM time intervals, the respective data points in the Langmuir probe data can be filtered out. The resulting inter-ELM profiles of Γ i , derived from j sat , and of T e are shown in figure  7  for both experiments. Unfortunately, the Langmuir probe at the poloidal position of the lower gap samples was defective during the reversed I p and B t experiment, while during the default I p and B t discharges it did not provide data of sufficient quality for the evaluation of T e.
Post-exposure SEM analysis As a first step of quantitative post-exposure characterisation, the samples were analysed in the Helios (FEI) and Auriga (ZEISS) SEM microscopes at IPP Garching. The relative abundance of both Pt in the marker layer and W deposited in the gap during plasma exposure were determined by EDX. X-ray spectra were acquired at 30 keV acceleration voltage with electron beam scans across rectangular areas of 250 × 210 µm 2 from the gap entrance down into the gap up to a depth of 5 mm where the Pt layer was shielded against plasma impact by the sample's steel spacer. The latter data point was initially assumed to still represent the pre-exposure marker thickness, although subsequent analysis revealed that the marker layer had been slightly damaged by the mechanical pressure of the spacer against the surface.
Acknowledgments This work has been carried out within the framework of the  EUROfusion Consortium , funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No. 101052200-EUROfusion ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the  European Commission. Neither the  European Union  nor the  European Commission  can be held responsible for them. Part of the work was performed under the  EUROfusion Work Package PWIE.
New YSO Candidates There are 276 point sources that we identify as new YSO candidates in this work, which are shown in the right panel of Figure  6. The stringent YSO color-color or color-magnitude cuts implemented by  Whitney et al. (2008) ,  Gruendl & Chu (2009) , and  Seale et al. (2014)  led to these sources being removed from the final catalogs from the respective papers. Whitney et al. (2008)  used a series of color-magnitude cuts in their query of the SAGE database using IRAC bands and the MIPS 24 μm band. Gruendl & Chu (2009) , on the other hand, used two simple cuts: [4. 5] -[8. 0] > 2. 0 to exclude normal and evolved stars, and [8. 0] > 14 -([4. 5] -[8. 0]) to exclude background galaxies. Seale et al. (2014)  require the presence of 24 μm emission as a proxy for star formation to identify possible YSO candidates.
The intercepts and the slopes of the best-fit line in the size-line width relation indicate that more energy might be getting injected into the clumps closer to R136. We assume that the line widths are dominated by nonthermal broadening (i. e. , turbulence). Observations by  Kalari et al. (2018)  of three clouds in 30 Doradus within 20 pc of R136 indicate that feedback from massive stars also contributes to the measured line widths. Kalari et al. (2018)  present 12 CO observations using data from the Swedish-ESO Submillimetre Telescope (SEST). The line widths they observed are large for a given cloud size in comparison to Larson's empirical relation, and this increase in observed line width is likely because the molecular gas is being pushed away by the ionizing radiation produced by the massive stars in R136  (Kalari et al.
The abundance-growth space for a synthetic E. coli community To illustrate this method, we first consider a synthetic cooperative community com posed of two E. coli strains auxotrophic for leucine (eco_L) and lysine (eco_K) (File S1, https://github. com/mathomics/ecosystem), studied in reference 37. Consumption and production of these amino acids were constrained based on their reported fluxes (Materials and Methods). In this community, cross-feeding of leucine and lysine is critical for survival (Fig. 1 ). We obtain an abundance-growth space whose shape resembles a concave trian gle (Fig. 1B ). Its dimensions are influenced by changes in the production capacity of leucine and lysine, as well as its external supplementation (Fig. S1 ), as discussed below. This shape shows that at high growth rates, abundances need to be balanced (f eco_K ∼ f eco_L ), which is consistent with reference 37.
On the other hand, communities with a dominant strain can only grow at lower rates, which was attributed to the less abundant strain being unable to satisfy amino acid requirements from the other  (37). Indeed, at a fixed abundance of eco_K (eco_L), the minimum flux value for the produc tion of leucine (lysine) reaches the upper bound in the model, making it unfeasible for greater growth of the community (Fig. S2 ). From the 5,068 reactions of the model, a cluster-partition is performed. Eight clusters allow us to distinguish relevant changes in this space (Materials and Methods and Fig. S3a ). We distinguish 116 reactions whose consensus values change between clusters, 1,880 that are unable to carry any flux in the abundance-growth space, and 2,371 presenting fixed non-zero flux values (File S2, https://github.
The abundance-growth space for a bioleaching community To test the method in an environmental community, we consider a simple microbial consortium between A. ferrooxidans Wenelen and S. thermosulfidooxidans Cutipay (see File S3 and File S4 in https://github. com/mathomics/ecosystem and Materials and Methods), obtaining a community competing for inorganic energy sources (Fe(II) and thiosulfate). Both bacteria are able to fix carbon available as CO 2 but only Cutipay can consume organic matter made available by the degradation of community biomass, which is modeled as a pseudo-reaction transforming a fraction α of the biomass produced by the community (Materials and Methods). We consider the case study where α = 0. 2 and a substrate level composed of energetically equivalent amounts of Fe(II) and thiosulfate.
Analysis of a synthetic E. coli community A community defined by two strains of Escherichia coli auxotrophic for leucine and lysine was selected from reference 37. The E. coli genome-scale model iAF1260 (59) was modified by constraining reactions associated to gene knock-outs made in reference 37: diaminopimelate decarboxylase (DAPDC) for the lysine auxotrophic strain (eco_K) and 2-isopropylmalate synthase (IPPS) for the leucine auxotrophic strain (eco_L). Glucose availability was set up to be 10 (mmol/g DW org /h), amino acid production was set up to be 0. 2 (mmol/g DW org /h) for both leucine and lysine, and consumption for both amino acids for both strains was constrained according to reported values of production and consumption stated in reference  (37). A of ℓ = 50 points is generated according to what was described previously, obtaining that from the 2,500 potential points of the grid, only 973 are feasible given how consumption and production constraints set up for this model are affected by community composition (File S1, https://github.
ferrooxidans ATCC23270 (66) was used as a reference  (67). Drafts for both A. ferrooxidans Wenelen and S. thermosulfidooxidans Cutipay were manually curated. Particular attention was given to iron and sulfur oxidation metabolism  (68, 69). Pathways from these subsystems were completed after including reactions from the literature (File S3, https://github. com/mathomics/ecosystem)  (68) (69) (70) (71). Since no biomass composition information was available for either of the bacteria studied, adaptations of biomass functions from models iMC507 and iHN637 were used for Wenelen and Cutipay, respectively. Both models were checked using Memote to ensure their quality before publication  (72, 73). Final metabolic models for A. ferrooxidans Wenelen (iML510) and S. thermosulfi dooxidans Cutipay (iSM517) are available at the following address: https://github.
This was done because glucose can be easily assimilated by the metabolic network (File S3, https://github. com/mathomics/eco system), as well as that it can be incorporated to synthesize nucleotides, amino acids, as well as other simpler organic acids found in bioleaching environments, such as acetate. The set of 1,717 reactions of this community model comprises 1,668 reactions from the original single models and 49 reactions that appear in the construction of the community model. The set of 1,611 metabolites of the community model compri ses 1,564 metabolites from single models and 47 metabolites from the community construction. Definition of parameters and scenarios for simulations of bioleaching communities. Different external and internal conditions are explored to determine how these scenarios change the distribution of qualitative states in the abundance-growth space.
FUNDING Funder Grant(s) Author(s)  Agencia Nacional de Investigación y Desarrollo (ANID )  FB2100005   Alejandro Eduardo Maass Natalia E. Jiménez Maria Paz Cortés Vicente Acuña   Ministerio de Economía ,  Fomento y Turismo, Chile (Ministerio de Economía, Fomento y Turismo )  ICN2021 044   Vicente Acuña Natalia E. Jiménez Alejandro Eduardo Maass Maria Paz Cortés.
Hippocampal subfields (CA1, CA2, CA3, dentate gyrus [DG], subiculum [SUB]) and extrahippocampal regions (entorhinal cortex [ERC], Brodmann areas 35 and 36, and parahippocampal cortex [PHC]) volumes were estimated automatically on averaged highresolution T2-weighted images using the Automated Segmentation for Hippocampal Subfields (ASHS) software together with a custom atlas  37. We used a custom atlas because recent observations based on ex vivo MRI and histology data showed that the dark band, a hypointense line appearing within the hippocampal GM on T2 images frequently used in segmentation protocols as the boundary between DG and SUB/CA, is composed of the stratum lacunosum moleculare  38. Thus, dark band voxels should be labeled as SUB/CA only, but were splitted between SUB/CA and DG in the original atlas provided with ASHS (ashs_atlas_upennpmc_20170810)  37.
Consents All participants included in the Age-Well RCT of the Medit-Ageing European project gave their written informed consent prior to the examinations. The Age-Well RCT was approved by the ethics committee (CPP Nord-Ouest III, Caen; trial registration number: EudraCT: 2016-002441-36; IDRCB: 2016-A01767-44; ClinicalTrials. gov Identifier: NCT02977819)  35. Medit-Ageing Charter, by any research team belonging to an Academic for carrying out a scientific research project relating to the scientific theme of mental health and well-being in older people. The Material may also be mobilized by non-academic third parties, under conditions, in particular financial, which will be established by separate agreement between Inserm and by the said third party. Data sharing policies described in the Medit-Ageing Charter are in compliance with our ethics approval and guidelines from our funding body.
Sex-stratified analyses Differences in demographical, sleep and MTL data between men and women are displayed in eTable 7 in the Supplement. Male participants exhibited greater AHI scores, and lower whole hippocampal, ERC, PHC, subiculum, CA1 and DG volumes. Female participants had greater memory scores at all time points, despite overall lower education level. Sex-stratified analyses revealed that the AHI was negatively associated with GM volume in the whole hippocampus (r=-0. 30, P=0. 01), ERC (r=-0. 50, P<0. 001), PHC (r=-0. 34, P=0. 003), CA1 (r=-0. 26, P=0. 02) and DG (r=-0. 30, P=0. 01), only in women (Table  5 ). Similar associations were obtained with the ODI and RMI. When stratifying female participants according to amyloid status, these associations were only significant in amyloid-positive women (Table  5 ).
Surgical technique MAKO robotic platform planning software (Mako, Stryker Corp. , Mahwah, NJ, USA) allowed preoperative implant planning using a patient-specific CT-based bone model and virtual implant templates. As previously shown, the 3D implant model followed the bony anatomy and had 1 mm of accuracy  [12]. The CT scan-based preoperative planning using a three-dimensional (3D) model created by the software allowed individualized planning, implant sizing, and adjusting the position to suit the functional alignment principles. The positioning of the femoral implant respected the following principles: the coronal plane was included between 6°valgus and 3°varus, matching the lateral distal femoral angle (LDFA), the sagittal flexion plane was limited to 10°of flexion to avoid notching. The femoral rotation followed the posterior condylar axis (PCA) ± 3°and trans epicondylar axis (TEA) between 3°internal rotation and 6°external rotation.
Measurement technique All measurements were performed using MAKO robotic platform planning software (Mako, Stryker Corp. , Mahwah, NJ, USA). A calibrated scale in millimeters allowed accurate and reliable measurements, with an accuracy of 1 mm. The measurements of the restoration of the trochlear offset after TKA were performed by two independent reviewers (an orthopedic surgeon and a medical student) for all measurements to assess the reliability of each measurement. To determine intraobserver variability, the second observer measured the patients twice, separated by a four-week interval. Both reviewers were trained on the MAKO platform to acquire the measurement technique. For the trochlear offset, we measured the restoration of the trochlear thickness in several positions in the medial, lateral, and central parts of the trochlea.
Statistical analysis Statistical analysis was performed using the XL STAT software (Version 2021. 2. 1, Addinsoft Inc. , Paris, France). The inter-and intra-observer reliabilities of the measurements were evaluated by an intraclass correlation coefficient. The strength of agreement for the kappa coefficient was interpreted as follows: < 0. 20 = unacceptable, 0. 20-0. 39 = questionable, 0. 40-0. 59 = good, 0. 60-0. 79 = very good, and 0. 80-1 = excellent  [14].
In this study, accelerometric data from seven Japanese buildings under long-term monitoring were analysed to explore the variability of the buildings' co-seismic response over time and its within-and between-building components, using co-seismic capacity curves developed in accelerationdisplacement-response-spectrum format. The data include the 2011 Tohoku Mw9. 1 earthquake, which caused building damage of different levels of severity, and the time-varying actual capacity curves were analysed considering earthquakes before and after 2011. We observed that the initial slope of the capacity curves reflects the amount of damage. The between-building and within-building components of the variability are discussed by comparing a single building and several buildings in the same class for several earthquakes.
g. , materials, lateral load resistance system, number of storeys, etc. ) to cover a wide range of building classes in the exposed area. In urban-specific seismic risk studies, it is common practice to start by assessing the exposure model at the site concerned and then to attribute class-capacity curves to each building  (Crowley et al. , 2021). This approach offers the potential advantage of taking into account a large set of buildings, but specific attributes within a class can cause significant deviations from the generic model. For example,  Martins and Silva (2021)  released a databank of capacity curves that has been used to represent the vulnerability classes of current European buildings  (Crowley et al. , 2021). For each class, the backbone capacity curves were compiled from simulated design-based research studies, i.
1 ). Ultimately, the total variability σ for a single building is given by: 𝜎 = #𝜏 ! + 𝜙 ! Eq. 1 The main objective of this study is to develop a chain of processing to obtain experimental capacity curves using strong motion data recorded in buildings and to quantify and process the variability of the building response, taking into account the building class adjustment to capacity curves provided by  Martins and Silva (2021). The dataset is described first, the selected buildings are presented in the second section, and the methods used to adjust the capacity curves from experimental data are discussed in the third section. The fourth section presents the results, and the paper ends with a discussion and conclusion.
Conclusion Experimental data from buildings are crucial for quantifying, understanding and processing the variability of the seismic building response and for identifying the origin of uncertainties through residual analysis. Experimental data offers the advantage of including the complex physical processes that affect buildings during seismic loading, and consideration of the time-varying capacity due to the actual condition of the building. In this study, we used strong motion data collected from seven Japanese buildings equipped with permanent instrumentation and post-processed in the NDE1. 0 database  (Astorga et al. , 2020)  to investigate variability in the co-seismic capacity curves. We developed an ADRS-based framework, considering the method proposed by  Freeman et al. (1999). We explored the within-building variability related to the building-specific response for a given earthquake, including its change in condition, and the between-building variability related to the drift of building-specific and class-specific models, which are derived from experimental data or selected from the GEM-Taxonomy models.
Consequently, the response variability of a given building in a given class differs from that of the class as a whole, which contradicts the ergodic assumption that the response variability for a class of buildings is the same as the response variability for any given building in that class. Time-varying experimental capacity models are useful for calculating time-varying seismic risk, especially after strong earthquakes or during a mainshock-aftershock sequence. Furthermore, the difference observed between experimental curves and the generic capacity curves obtained from the global databank of capacity curves can quantify the adjustments required to account for a building's actual structural condition. Using the model developed in this study, we derived experimental capacity curves that took into account the damage-related condition and the necessary adjustment of the generic capacity models by reducing epistemic uncertainty.
These draw from the resource opportunity offered by vast quantities of publicly available online data or 'Big data', combined with the epistemic influence of the blooming computer science techniques of natural language processing and machine learning. Although these data-intensive techniques may promise better insight, decision-making, and even empowerment  (Williams, 2022) , they are also the technical core of the current age of surveillance capitalism  (Zuboff, 2019). Given this origin, social listening could feed the already problematic drive for cost-effective, technical and reductionist solutions to chronic and emerging issues in Global Health, if this practice and its methods are not reviewed. Despite the WHO's emphasis on incorporating both online and onsite data for social listening  (Cunard Chaney et al.
, 2021) , our analysis of the top 20 cited papers on the topic in relation to COVID-19 (Supplementary Material, Table  S1 ) revealed a predominant reliance on online data. Only two studies incorporated an onsite data collection component, with the remaining four being reviews or commentaries without primary data. This suggests that in practice, social listening in global health remains largely tied to its roots in online marketing data analysis. While social listening provides easy access to public discourse and helps identify emerging vaccine narratives  (Greco & Polli, 2019)  and can be an asset in context of limited access to the field (lockdowns), its reliance on online data presents significant limitations, especially concerning representation. For instance, social media platforms like Twitter and Facebook, commonly used for online social listening, have a user base skewed towards males and the well-educated  (Statista, 2022 (Statista, , 2023)).
Displaced from their context, discourses lose authenticity. Analyses relying only on online discourses fail to capture this embedded truth-making, leading to simplistic misrepresentations of context on behalf of Global Health authority. These reductions simplify complex social structures into measurable markers, challenging our understanding of how these systems operate. In light of the interconnected discourses revealed in our study, we argue for the advancement of social listening methodologies. While online-only methods provide quick, cost-effective access to public discourses on vaccines, they are inherently limited by data representation biases and a proclivity to prioritize information accuracy over understanding people's concerns. Additionally, social media-based narratives are often constrained by factors like text length, user identity, context, and real-life implications of shared opinions.
This over-reliance on online discourses tends to transform social listening into a superficial exercise of social hearing, rather than listening, potentially leading to inaccurate representations and consequent issues. We propose an effective approach to social listening through grounded data collection, such as in-depth, onsite ethnographic research. This approach provides context, offers insights into real-life decisions, and grants access to a wider array of voices, necessitating field interaction and data collection. Grounded social listening allows us to recognize and understand individuals' pragmatic negotiations of power relations and hierarchies within their societies, thereby providing a more comprehensive understanding of their responses to health policies. In an era of global health dominance, we observe a reductionist tendency to simplify intricate social realities into quantifiable markers.