dataesr/scientific-paragraphs-categorization huggingface dataset:
(2020b)  (https://data. mendeley. com/datasets/4fhyt4fbyd/1) and are mainly based on fossils housed at Yunnan University  (Hou et al. , 2017)  and collected from the North East of Fuxian Lake and West of Dianchi Lake in the Yunnan Province (i. e. , 13 sites in total: Dapotou, Ercaicun, Erjie, Fengkoushao, Hongjiachong, Jianshan, Laogaoshan,  Ma'anshan, Mafang, Maotianshan, Meishucun, Shankoucun, and Xiaolantian). The average number of tissues per taxon for each group was calculated. This can be used as an index to assess the preservation of groups within the Chengjiang biota. However, its value could be impacted by the anatomical heterogeneities between groups. For instance, most living euarthropods have at least either a biomineralized or sclerotized exoskeleton, non-sclerotized cuticular parts, and internal organs, resulting in a high value (three tissue types per genus during the life of the organism).
In this sense, P(E) and its associated 95% confidence intervals were plotted to constrain group-specific preservation in the Chengjiang biota. These 95% confidence intervals were calculated using a Bayesian approach with a null prior to account for the small sample sizes of some groups. Animal groups with a high number of preserved internal organs are considered to be better preserved in general than those with a lower proportion of fossilized internal organs. The proportion of internal organs preserved were then compared to the generic diversity and the abundance data for each group as taken from  Hou et al. (2017). We acknowledge that there might be some contrast between the fossils included in this paper when compared with collections at other institutions, or even that some contrasts might exist between different localities within the Chengjiang biota.
g. , Cambrian priapulids) are automatically buried at deeper levels within the sediment, slowing down their decay  (Pratt & Kimmig, 2019). Epibenthic, nektobenthic, and nektonic taxa are able to escape, to some extent, the flow and only exposed carcasses of these animals are preserved (very long decay time). The interaction between modes of life and preservation is not limited to the Chengjiang biota and other Burgess Shale-type deposits. For example, it was long assumed that the rarity of ten-armed Decabrachia   (Sansom, 2016) , Onychophora  (Murdock et al. , 2014) , and Chordata  (Sansom, Gabbott & Purnell, 2010)  as observed in decay experiments. Note that for Euarthropoda  (Butler et al. , 2015)  there is no differentiation between internal organs in the literature as only the time of disappearance of all tissues is considered.
3C ). Ctenophores and cnidarians are other examples of this dilemma (Figs. 3D-3G ). Paleontologists often rely on complete specimens in order to say if a particular fossil belongs to cnidarians or ctenophores and even then, it is contentious  (Daley & Antcliffe, 2019). It is almost impossible to recognize any fossil fragment belonging to these groups if it has not been described previously. Even when nearly complete specimens are discovered, it will be still challenging to assign those carcasses to either cnidarians or ctenophores because defining and recognizing characters can be ambiguous  (Daley & Antcliffe, 2019; Zhao et al. , 2019). The possibility of paleontologists unconsciously and unwillingly inducing variations in a certain dataset is not limited to the ability to identify a certain fossil fragment.
Preservational variations can also be induced by fossil collection, preparation, accession, and digitization  (Whitaker & Kimmig, 2020). Variations can be induced following certain practices such as sampling from a preferred locality, or the background, interest, and specialization of the sampling team  (Whitaker & Kimmig, 2020). However, within the context of this paper, we consider that induced preservational variations remain minimal. Sampling for the fossils that are currently housed at Yunnan University was always made by a group of international researchers covering numerous aspects of paleontological research. Future investigations can reveal different patterns in fossil collections at other institutions. Quantifying the divergence between institutional collections will be key to quantifying collection biases and understanding the impact we and other colleagues might unconsciously introduce in databases.
CONCLUSIONS The preservation potential of the Chengjiang biota was analyzed using occurrence data. It is shown that abundant groups are not necessarily the best preserved in terms of internal organs. The differences observed between groups can be explained by the ecology of living animals, and by processes such as transport and decay. It is worth noting that the results of decay experiments and fossil-based datasets are complimentary in resolving preservational patterns in the rock record and are mutually illuminating as we search for a deeper understanding of complex preservation processes. The differences in preservation observed between animal groups within the Chengjiang biota highlight that future taphonomic   et al. , 2021) , (B) Laminacaris chimera CJHMD-00003  (Guo et al. , 2019). Chordate: (C) Myllokunmingia fengjiaoa RCCBYU-10200a  (Hou et al.
ACKNOWLEDGMENTS We want to honor  Jean Baptiste Joseph Fourier  in the bicentennial of the end of his master pieces, written at Grenoble between 1808 and  1822. Joseph Fourier  was a living example of the idea of a tailored to problem model. Taking up ideas from the  Bourguignon Buffon ,  Joseph Fourier  modeled fluid dynamics (work taken up by his Dijon polytechnic students Darcy and Navier) and cooling of the earth, making it possible to calculate its age. This last study has been taken up in an exciting scientific debate by  Lord Kelvin  and  Charles Darwin , after the latter's visit to Chile, namely, to the Campana massif. Quoting studies by Fourier and  Claude Gay  (pioneer, with  Louis Lliboutry , of the geology of Chile), Darwin explained with geologist's arguments that earth was several hundred million years old, while  Kelvin  calculated a few dozen, using a model mathematically rigorous but not tailored to the problem.
| DATA AND METHOD This research employs a mixed-method research approach (Hesse-Biber, 2010), combining quantitative and qualitative data  (Beziat et al. , 2021). 9 The quantitative data were derived from two different sources: a web-based questionnaire survey and count data. In this article, we draw mainly from the questionnaire survey, which is supplemented by insights provided by qualitative data from interviews and count data.
Such biases were apparent in our sample. The raw sample was characterized by the over-representation of women, 18-34-year-olds, residents of the municipalities of Lyon and Villeurbanne, working persons and those with higher education qualifications. Conversely, the over 60s and non-working persons were under-represented, as were the residents of the areas furthest from the centre. The data were weighted using INSEE population census 10 figures by the level of urbanization of the municipality of residence (or of a group of municipalities, when located in the outskirts and more rural zones of the survey perimeter), age and sex. For working people, an additional weighting was carried out, to correct the over-representation of higher education workers in the sample. This additional weighting factor straightens the structure of professions, without eliminating completely this bias in favour of 'higher intellectual and managerial professions'  (Beziat et al.
| Data on mode-by-mode changes in flows The flow data were obtained from permanent automatic hourly counts in the Lyon metropolitan area which have been available since January 2019. They were analysed by the CoviMob Observatory (see note 9). The database was obtained from 2,200 traffic counting stations, 70 bicycle counting stations, and ticket validation data in the case of public transport use. Once processed and aggregated, the count data provide a week-by-week description of changes in car, 11 bicycle and public transport flows from before the onset of the crisis.
This research is a part of CoviMob project (The Health Crisis and Daily Mobility) and is funded by the Lyon metropolitan authority, the public transport authority Sytral, IDEX Lyon and the CNRS. The flow data presented in Figure 3 were obtained from the observatory set up by the Arcadis consultancy, a partner of the project partners. The dissemination as well as the weighting of the web survey beneficiated from the collaboration of the Mobil'homme consultancy, another partner of the project. See CoviMob website: https://web. msh-lse. fr/covimob/.
Taxonomic sampling and data acquisition We sampled 638 species of mammals (670 specimens), representing 598 genera of 138 living families (Figure  1 ). Sampling varies from one to four individuals per genus. We provided micro-CT-scans and surface scans of 58 small to medium sized-specimens from different institutions (available online at MorphoSource. org, Table  S1  from SI 1), 23 of them previously used by Martín-Serra and Benson 2020. The digital dataset was combined with 351 meshes available on MorphoSource. org (Table  S1  from SI 1). Image stacks were converted into three-dimensional models using Avizo 8. 1. 1 (1995 -2014 Zuse Institute Berlin) , where scale dimensions were incorporated based on the voxel size of each scan. Data collection from the digital models was also conducted in Avizo 8.
1. 1 (1995 -2014 Zuse Institute Berlin). We complemented this dataset with measurements provided by caliper of 261 medium to large body-sized species from the mammal collection of the Muséum National d'Histoire Naturelle (Paris, France) (Table  S1 ). We measured 20 linear distances from anterior limb bones, including the humerus, the radius, the third metacarpal and the first phalanx of digit III. We acquired five measurements for each element: length, widths (proximal, mid-shaft and distal) and height (Figure  2 , see detailed description in Table  S2  from SI 2). We opted not to include the ulna because this bone is fused to the radius in many taxa (see  Sears et al. 2007) , preventing the acquisition of such measurements. The metacarpal and first phalanx of digit III were sampled because this is the only digit present in the hands of all mammalian lineages, even in groups that exhibit digit loss or fusion with other autopodial elements, such as in golden moles and ungulates  (Clifford, 2010; McHorse et al.
, 2019; Prothero, 2009). Each individual was measured twice with the subsequent calculation of the mean and standard error in order to verify measurement error. Body mass estimates were assembled from the PanTHERIA database  (Jones et al. , 2009)  and complemented by literature sources when necessary (Table  S1  from SI 1). Species taxonomy followed the Mammal Diversity Database published by  Burgin et al. (2018).
Comparative analyses Analyses were implemented in R 4. 1. 2 (R Core Team, 2021). We used the phangorn R package  (Schliep, 2011)  to estimate a maximum clade credibility (MCC) tree from a posterior sample of 10,000 trees published by  Upham et al. (2019). Because the incorporation of some species was available only at the genus level, we pruned the MCC tree to genus level, according to the taxa sampled by our study. Allometry generally explains most part of morphological variation, as body parts usually grow together, masking variation mediated by local development  (Marroig, 2007; Raff, 1996). Because we are particularly interested in understanding morphological constraints imposed by the local development of the limb, we decided to remove the allometric component of our dataset in order to reduce variation associated with other sources of development.
The OU model of evolution had the best fit for all the linear regressions accounting for body mass using the MCC tree (Table  S3  of SI 2). Thus, we simulated trait evolution under OU process on 100 datasets, in order to account for error and conduct the downstream analyses (function mvSIM() from mvMORPH;  Clavel et al. 2015 Clavel et al. , 2019)). We repeated the body mass PGLS for the simulated data and calculated the residual covariance phylogenetic matrices.
, 2009). We calculated the integration as the dispersion of the standard deviation of eigenvalues of our trait matrices, following  Pavlicev et al. (2009). For instance, highly integrated traits have most of the independent variance concentrated in the first few eigenvalues, while uncorrelated traits have the variance similarly distributed between eigenvalues  (Pavlicev et al. , 2009). Eigenvalue dispersion was inferred from CalcEigenVar() function of evolqg R package  (Machado et al. , 2019; Melo et al. , 2015) , which calculates the relative eigenvalue variance of the matrix as a ratio between the observed variance and the theoretical maximum for a matrix of the same size and trace  (Machado et al. 2019). Differences between distributions were computed by an ANOVA and detailed by Tukey Tests of the 95% CI.
Acknowledgements We thank the following collection managers at the  MNHN, Paris , for their support during data acquisition:  Alexander Nasole ,  Aude Lalis ,  Aurélie Verguin ,  Céline Bens ,  Géraldine Veron ,  Jacques Cuisin ,  Joséphine Lesur  and  Violaine Colin. Creation of datasets accessed on MorphoSource was made possible by the following funders and grant numbers:  NSF   DBI-1701713 ,  1701714 ,  1701737 ,  1702263 ,  1701665 ,  1701767 ,  1701769 ,  1701870 ,  1701797 ,  701851 ,  1702442 ,  1902105 ,  BCS 1317525 ,  BCS 1540421 ,  BCS 1552848 ;  ERC-2015-STG-677774  ( TEMPO Mammals  project to RB) and  Leakey Foundation. Project funding for digital acquisition of each used specimen is detailed in Table S1 of Supporting Information 1. We also thank  Anjali Goswami ,  Helder Gomes Rodrigues ,  Eric Guilbert  and  Loïc Kéver  for their insightful comments during the elaboration of this study.
Materials and Methods Hospitalization Data. Hospital data are obtained from the SI-VIC database, the national inpatient surveillance system providing real-time data on COVID-19 patients hospitalized in French public and private hospitals (SI Appendix, Supplementary Text). Smoothing. Hospital data follow a weekly pattern, with fewer admissions during weekends compared to weekdays, and they can be noisy at the regional level. Therefore, in the absence of smoothing or with simple smoothing techniques, forecasts can be biased depending on the day of the week on which the analysis is performed. In order to remove this day-to-day variation and obtain a smooth signal at each date not depending on future data points, we smooth the data using a 2-step approach based on local polynomial regression and the leastrevision principle  (17)  (SI Appendix, Supplementary Text and Figs.
We begin to make forecasts at tÀ1 because in real time, on day t, values at t and tÀ1 are not consolidated yet and the last reliable data point used for forecasts is the value at tÀ2. Model performance is evaluated using two main metrics. Our primary metric for point forecast error, the RMSE, is used to evaluate predictive means  (18) , and our secondary metric, the WIS, is used to assess probabilistic forecast accuracy  (1, 6, 19). The WIS is a proper score that combines a set of interval scores for probabilistic forecasts that provide quantiles of the predictive forecast distribution. It can be interpreted as a measure of how close the entire distribution is to the observation, in units on the scale of the observed data  (6). The WIS is defined as a weighted average of the interval scores over K central 1-α predictions intervals bounded by quantile levels ð α k 2 , 1 À α k 2 Þ: WIS α O:K ðF, yÞ = 1 K + 12 1 2 j y À m j + ∑ K k=1 α k 2 u k À l k ð Þ+ ðl k À yÞ1ðy < l k Þ + ðy À u k Þ1ðy > u k Þ , [ 1 ] where y is the observed outcome, F is the forecast distribution, m is the point forecast, 1(.
All predictors and data sources are described in SI Appendix, Supplementary Text and Figs. S2-S4. For each individual model, covariates are selected using a forward stepwise selection approach over the training period (not using data from the test period) (SI Appendix, Supplementary Text). In order to determine the importance of the different predictors and explore their effect on the growth rate, we retrospectively fit the best individual models from June 3, 2020, to March 6, 2021, on all regions together, whenever possible. We start the fit on June 3, 2020, when all the predictors are available. The parameters of the ARDL model vary with the prediction horizon, and those of the ARIMA2 model vary by region. Therefore, only four models (BRT, RF, MLR, and GAM2) are included in this subanalysis.
For s H and s ICU we use gamma survival functions with the mean to be estimated and the coefficient of variation (i. e. , the SD over the mean) fixed at 0. 9 for s H and 0. 8 for s ICU , while, analogous to g H , we take s HÀICU ðuÞ to be an exponential survival function with a mean of 1. 5 d. We minimize the sum of squared errors over the last 5 data points to estimate the free parameters of s H and s ICU. Once all the parameters are estimated and the forecast of H is available, we use the equations above to forecast H ICU , B H , and B ICU , assuming that all parameter estimates remain constant. To account for uncertainty in parameter estimates, we use the bootstrapped smoothed trajectories of hospital admissions to generate 95% prediction intervals for the three other targets. To assess the forecasts of ICU admissions and bed occupancy in the ICU and general wards, we report the MAPE because its interpretation in terms of relative error is straightforward, the 95% prediction interval coverage, and the RMSE.
ACKNOWLEDGMENTS. We are grateful to the hospital staff and all the partners involved in the collection and management of SIVIC data. We thank  Rapha€ el Bertrand  and  Eurico de Carvalho Filho (PREDICT Services ) for providing meteorological data. We thank  Google  for making their mobility data available online. Funding sources: We acknowledge financial support from the  Investissement d'Avenir program , the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases program  (grant  ANR-10-LABX-62-IBEID ),  Sant e publique France , the  INCEPTION  project ( PIA/ANR16-CONV-0005 ), the  European Union 's  Horizon 2020 research and innovation program  under grants  101003589  (RECOVER) and 874735 (VEO),  AXA ,  Groupama , and EMERGEN.
Acknowledgment  A. Corli  and  M. D. Rosini  are members of the  Gruppo Nazionale per l 'Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA)  of the  Istituto Nazionale di Alta Matematica (INdAM)  and acknowledge financial support from this institution. The second author acknowledges the support of the  Région Bourgogne Franche-Comté , projet 2017-2020 " Analyse   mathématique et simulation numérique d'EDP issus de problèmes de contrôle et du trafic routier ", and  Instytut Matematyki ,  Uniwersytet Marii Curie-Sk  lodowskiej, and  Dipartimento di Matematica e Informatica, Università degli Studi di Ferrara  for the hospitality during the preparation of the paper. The last author acknowledges the support of the  National Science Centre, Poland , Project " Mathematics of multi-scale approaches in life and social sciences " No.
In fact, as recalled by Li  [40] , outside of healthcare settings, existing ventilation standards do not account for infection control. When CO2 concentration exceeds threshold values, the ventilation flow rates are usually insufficient and aerosol route contamination risk is high as illustrated by Figures  2  and 3. In this context, we carried out, in autumn 2021, a series of CO2 concentration measurements and observations in various environments. Measurements consisted in determining the CO2 time evolution within each room using non-dispersive infrared (NDIR) CO2 sensors (Aranet 4 or ZG-106 Protronix CO2 monitor). Their accuracy was ±3% and ±5% of reading for the Aranet 4 and the ZG-106 Protronix respectively. The sensors are factory-calibrated and allow raw data logging with time stamps.
However, inhomogeneous infection patterns are reported for a number of well-documented transmission events in closed spaces, especially in restaurants  [14] [15] [16]  but also in other places such as aircrafts  [17]. Generally, in these specific well studied cases, inhomogeneity was created by the mechanical ventilation system of air conditioning (hereafter AC) with recirculation, inducing locally larger air velocity. One typical and largely mediatized event concerned a restaurant in Guangzhou, China. It has been the subject of numerical modeling  [14]. Numerous published works in the field do not relate to a specific observed event but to hypothetical situations supposed to represent typical cases, such as a supermarket  [18]. These models rely on CFD (Computational Fluid Dynamics) calculations of the air flow stream, using a variety of software, such as Open Foam for example.
S Y N T H E T I C DATA S E T S This work is based solely on results obtained on synthetic sets of Ly α data that mimic properties of the DESI Ly α surv e y. In this section, we describe the basic principles behind the production of these synthetic data sets, the particularities of DESI data, some special sets used to test our hypotheses, and how we mimic the intrinsic errors in quasar redshift estimates.
g. Peirani et al. 2014 ; Sorini et al. 2016  ) also rely on N -body simulations to calibrate a model used to produce quick lognormal mock catalogues. The method we use in this work to create synthetic data is based on correlated Gaussian random fields  (Coles & Jones 1991 ; Le Goff et al. 2011 ; Font-Ribera, McDonald & Miralda-Escud é 2012a ; Bautista et al. 2015 ) , whose correlations follow a given input power spectrum. The random field mimics the matter density field in the Universe. Quasars are placed via Poisson sampling in regions where the density is larger than a given threshold. One dimensional density 'skewers' along the line-of-sight to each quasar are drawn by interpolating the same random field. These can then be transformed to a transmitted flux fraction, representative of a Ly α forest, by adding small-scale MNRAS 516,  421-433 (2022)  fluctuations, converting from density to optical depth, and adding redshift-space distortions (RSD).
We used an implementation of the Gaussian method of synthetic Ly α forests, named LYACOLORE  (Farr et al. 2020a ). COLORE mock catalogues use the package COLORE 2  (Ram írez-P érez et al. 2022 ) to place quasars in a lognormal density field. Skewers are drawn from each quasar, have small-scale power added to them and these are transformed into transmitted flux fractions using the LYACOLORE package. 3 This process produces noiseless transmission fields that have correct clustering properties on scales rele v ant for BAO studies. LYACOLORE stores skewers of metal absorption and a table of high-column density systems (HCDs) in its output, which may be optionally added to the Ly α skewers during subsequent stages of the pipeline. These simulated data sets co v er the projected five year DESI footprint and contain around one million quasars in the redshift range z ∈ [1.
Simulating quasar redshift errors Even though we try to capture the diversity of quasar continua in our mocks, fitting algorithms on simulated data often perform better than in real data (F arr, F ont-Ribera & Pontzen 2020b ). For this reason, instead of trying to estimate redshifts from our mock spectra, we decided to emulate errors in the pipeline redshift estimation. We add Gaussian random errors to the quasar redshifts in the mock catalogues, using by default an r. m. s. of σ v, z = 500 km s -1. It is important to highlight a key difference between how we simulate FoG and redshift errors. Even though they both add random shifts to the quasar redshift, the shift emulating FoG is applied before we generate the quasar continuum. On the other hand, the shift emulating redshift errors only affects the value of redshift in the quasar catalogue that will be used in the analysis, but it does not change the simulated spectrum.