dataesr/scientific-paragraphs-categorization huggingface dataset:
SFR Density, Main-Sequence, and Dust Abundance across Cosmic Time The data presented Section 3. 1 suggest that a significant fraction of the UV light is obscured in main-sequence galaxies. In this section, we present more detailed results on the dust abundance and the implication on the total cosmic SFR density. The fraction of dust-attenuated and -obscured star formation in the ALPINE sample is studied by Fudamoto et al. [92]  using two diagnostic diagrams, namely, the IRX-β and the IRX-M * relations. The measurements of the UV continuum slope (β), UV luminosity, and stellar mass are described in detail in Faisst et al. [26]. In brief, the quantities are determined from the UV to optical best-fit SED to the broad and narrowband photometry of the galaxies. The β slope ( f λ ∝ λ β ) is measured between a rest-frame wavelength of 1300 Å and 2300 Å, consistent with previous studies (e.
Acknowledgments: M. R. acknowledges support from the  Narodowe Centrum Nauki  ( UMO-2020/38/ E/ST9/00077 ). G. C. J. acknowledges  ERC Advanced  Grant  695671  " QUENCH " and support by the  Science and Technology Facilities Council (STFC). YK acknowledges the support by funding from the  European Research Council Advanced  Grant  ERC-2010-AdG-268107-EARLY. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2017. 1. 00428. L. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. This research made use of Astropy (http://www. astropy.
e. , twice a year) with mineral fertilizer (40 g N m -2 y -1 ), pig slurry compost (5 kg m -2 y -1 ) or poultry litter compost (7 kg m -2 y -1 ). The main OF properties are given in Table  S1. The mean annual inputs of Cu and Zn to soil by organic and mineral fertilizers were calculated from the dataset provided by  Laurent et al. (2019). They were much higher for OF (i. e. , 1 to 12 and 4 to 66 mg kg -1 soil for Cu and Zn, respectively) than for mineral fertilizers (i. e. , 0. 004 to 0. 031 and 0. 036 to 0. 128 mg kg -1 soil for Cu and Zn, respectively). The mean annual inputs of Cu and Zn to soil by OF in the three field trials was also higher than those calculated at the French level  (Belon et al. 2012). We subselected 20 out of the 74 soil samples selected by  Laurent et al. (2020)  from a soil library of these three field trials to encompass the fertilization types and the gradient of Cu and Zn contamination, pH, and organic matter concentration that could influence the Cu and Zn availability in soil and bioavailability to earthworms.
Data processing and analysis Datasets are accessible via Dataverse  (Laurent et al. , 2021). Data were log10 transformed and analyzed with R packages  (R Core Team, 2016). Statistical significance was considered when p  0. 05. Principal component analysis (PCA) was performed using the FactoMineR and factoextra packages to illustrate the respective effects of soil or fertilization types on the solution chemistry of soils and Cu and Zn availability proxies (i. e. , total Cu and Zn concentrations and free ionic activity) in the presence of earthworms. The effect of fertilization types (none, mineral, or organic) on the solution chemistry of soils in the presence of earthworms and on Cu and Zn bioavailability proxies (i. e. , net Cu and Zn accumulation and Cu and Zn concentrations in earthworms) to earthworms was assessed for each soil by performing a one-way analysis of variance (ANOVA) and the Tukey honestly significantly different (HSD) post hoc multiple test comparison when conditions of residual normality and homoscedasticity were satisfied, or otherwise the Kruskal-Wallis test and the Dunn post hoc multiple test comparison.
For the three soil types considered together, structural equation models (SEMs) using the R package lavaan were built to identify and mathematically characterize the respective contributions of the contamination (DTPA-extractable Cu and Zn concentrations) and the solution chemistry (pH, DOC, and SUVA) of soils to Cu and Zn availability proxies in soils and to bioavailability to earthworms. The indirect effect of the contamination and the solution chemistry on Cu and Zn availability proxies and on bioavailability to earthworms was accounted for when building SEM. The fit of the model was evaluated using three indicators: the comparative fit index (CFI), the root mean square approximation (RMSEA), and the standardized root mean square residual (SRMSR). The adequacy of the fit of the models was reflected by CFI values above 0.
Effect of the types of soil and type of fertilization on soil solution chemistry and Cu and Zn availability Soil and fertilization types distinctly influenced the distribution of the dataset on the chemistry and Cu and Zn availability proxies (i. e. , total Cu and Zn concentrations and Cu 2+ and Zn 2+ activities in soil solutions) in the solution of earthworm-inhabited soils (Fig. 1 ). The first component (PC1) was determined mainly by Cu and Zn availability proxies, pH, and DOC concentration. The second component (PC2) was determined mainly by SUVA (Fig. 1a ). Soil types were dissociated into three clusters and mainly along PC2 (Fig. 1b ). Unlike the soil types, the fertilization types were dissociated into two main clusters along PC1 corresponding to mineral and organic fertilization, with the two data points corresponding to the no fertilization treatment in between (Fig.
Acknowledgments We thank  E. Tillard ,  E. Rivière ,  P. Lecomte ,  E. Doelsch ,  J-F. Mayen , and  O. Salmacis  ( Cirad ) for sharing data and soil samples. We are grateful to  M. Razafindrakoto  for her help finding D. saliens in the three field trials. We are grateful to  M. Collinet ,  J. Idmond ,  M-F. Gauvin ,  C. Gauvin ,  A. Lombard ,  C. Chevassus-Rosset ,  M. Montes ,  M. Tella  ( Cirad ),  R. Freydier  ( CNRS, Hydrosciences Montpellier ), and  L. Chiarello  ( Université Montpellier ) for their involvement in the laboratory experiments and analyses. We also thank  Y. Capowiez ,  T. Lebeau ,  C. NGuyen ,  A. Péry , and  E. Smolders  for their advice.
Assessing the nature of the relationship between twinning propensity and intrinsic fertility requires not only an analytical framework that accounts for differences in risk exposure within and between mothers, but also large amounts of reliable data. Modern datasets conflate natural variation in twinning propensity and intrinsic fertility with variation introduced by medical intervention and family planning, whereas historical datasets for single populations are often too small to study rare events such as twinning with precision (a notable exception being the dataset from the Utah population studied by Robson & Smith  9, 23  ). We thus assembled a unique demographic dataset by pooling together data from nine European populations (Table  1 ), resulting in 105,833 births to 21,290 mothers born in the 18th and 19th centuries  5, 8, [31] [32] [33] [34] [35] [36].
These mothers were from populations that had not yet undergone much of the demographic transition towards lower lifetime number of offspring that is characteristic of industrialised populations  37. For the sake of simplicity, we considered all births resulting in more than one child as twinning events. Such events therefore include rare cases of triplets. Furthermore, although our dataset does not distinguish dizygotic from monozygotic twins, estimations based on the sex of children suggest that the vast majority of twins in our dataset (~80%) were dizygotic, similar to other European populations  1, 3. We start this study by replicating previous analyses of the relationship between lifetime twinning status and total births after aggregating birth outcomes within mothers (i. e. without accounting for variation in risk exposure between mothers).
3 ). Each of these hypothesised mechanisms combine three generalised linear mixed-effects models (GLMM)-modelling, respectively, the three key life-history events recorded in our dataset: parity progression (whether or not a mother reproduces again after having given birth), interbirth interval (the duration between two consecutive births) and the twinning outcome of a birth (whether a birth yields a singleton or multiple children). All hypothesised In mechanism P, a twinning event impacts Parity progression. A drop in parity progression after a twinning event is expected since such an event may increase the risk of maternal death and reduce reproductive health  3, 5, 40. Furthermore, mothers may show a reduced inclination to continue reproducing after the birth of twins, as suggested for some modern populations 41, but see  42.
In particular, it considers that maternal age influences both the per-birth twinning probability (S1) and total births (S2), giving rise to a relationship between these variables. This could happen, for example, because women starting to reproduce late in their life are more likely to both produce twins and have fewer births (as mentioned above). As for the other life-history traits, we quantified the effect of maternal age on the per-birth twinning probability while controlling for parity since both variables are strongly correlated (Spearman's rho = 0. 69 in our dataset) and potentially associated with twinning  1. The GLMM fitted to our data is consistent with S1: we replicated the well-established result that the maximal per-birth twinning probability is reached when mothers were in their mid to late thirties 11 (Fig.
The effect of the schedule of reproduction (mechanism S) is more ambiguous as it could have influenced the relationship either way depending on the exact reproductive schedule of mothers. Contribution of the candidate mechanisms to the relationship between twinning and total births. To assess the role of each of these four mechanisms in shaping the relationship between perbirth twinning probability and total births, we used the estimates provided by the three fitted GLMMs illustrated in Fig. 4  (as well as sub-models derived from them; see Methods for details, as well as Supplementary Tables  8 9 10 11 12 ) to parametrise individualbased simulations. We programmed the simulations to replay in silico the reproductive history of the women from our dataset, birth after birth, by drawing random realisations of the three key life-history events in the presence or absence of each of the four mechanisms (Supplementary Figs.
3, 4 ). After simulating multiple datasets under each of the 16 possible combinations of the four mechanisms (PISH, PIS, PIH, PSH, PI, …, Fig. 5 ), including a scenario that does not contain any such mechanism (named 0), we quantified the relationship between per-birth twinning probability and total births in each simulated dataset, and compared these to the relationship observed in the empirical data. We performed this comparison using a so-called goodnessof-fit test which considered, in turn, each simulation scenario as a null hypothesis which the empirical data had a chance to reject (see Methods and Supplementary Notes for details). This approach is suitable for identifying those hypothesised scenarios that can explain the data, and for excluding those that cannot. Importantly, accepting any single scenario from the set of those retained does not imply that it is the true one.
Furthermore, none of the scenarios will be retained if all fail to fit the data adequately. Our analysis of the simulated datasets revealed that the relationship shown in Fig. 2  is most likely the result of the impact of a twinning event on parity progression (mechanism P) and to a lesser degree on interbirth intervals (mechanism I), as well as the effect of the schedule of reproduction on per-birth twinning probability (mechanism S). Indeed, all six simulation scenarios not rejected by our goodness-of-fit test (i. e. p > 0. 05) include mechanism P (scenarios P, PI, PS, PIS, PSH, PISH; Fig. 5 ; Supplementary Table  13 ) and four of them include mechanism S (PS, PIS, PSH, PISH). Simulating heterogeneity in twinning propensity between mothers (mechanism H) did not increase the goodness of fit of the eight simulation scenarios that did not initially consider this mechanism (0, P, I, S, PI, IS, PIS), with the exception of the goodness of fit for PS, which was marginally improved.
Under the eco-evolutionary hypothesis, a weak relationship is expected if populations are close to their evolutionary equilibrium. In contrast, the heterogeneity hypothesis would result in a stronger relationship established by condition dependence. Irrespective of the hypothesis, trait, population and species under study, it is likely that much research in life science and medicine is impacted by the problem we have highlighted-an ecological fallacy caused by aggregating life-history events within individuals. Flagging which particular results or claims are robust to the effect of data aggregation requires reanalysis using statistical approaches that do not rely on aggregation, such as hierarchical models  27. The most effective way to avoid the issue in the first place is for data providers to distribute non-aggregated datasets.
Methods Data preparation Historical data. The primary source of data is historical parish registers, which have been transcribed under the supervision of many of the study authors over a number of decades, primarily for evolutionary demographic research. Our dataset (Supplementary Data 1) includes nine European populations, including some for which the positive relationship between maternal lifetime twinning status and maternal total births has been described previously  5, 6, 8, 10. Details for the populations used in this study are given in Table  1  and in Supplementary Table  14. The sourcing of each dataset and the socio-ecological background of each population have already been described in previous studies (see Table  1  for references). Overall, there is no reason to suspect a high level of consanguinity in these populations  62  , so our analyses do not account for the variable level of relatedness between individuals.
The datasets cover pre-industrial periods in which the lifetime reproductive success was high and the majority of people were living and working in agrarian communities, except for the Samis (from northern Finland and Sweden) who made their living fully or in part from a combination of herding, fishing and hunting. The smooth decline of the probability of parity progression with parity (Fig. 4a ) suggests that mothers did not effectively limit their reproductive success with the aim of achieving a small family size, as found in populations that have undergone a demographic transition. Data selection. We use the term family to describe a mother and all individuals to whom she gave birth over her life. For our analyses, all families considered met the following criteria: the mother's age was known at a monthly resolution and her life course traced until at least age 45 (approximating full reproductive life), the birth year and month of all offspring must have been recorded and consecutive births were all at least nine months apart from one another.
We use the term twin(s) to refer to offspring who were the result of the same multiple birth (including 1745 sets of twins sensu stricto and 19 sets of triplets in the filtered dataset and, respectively, 1915 and 20 in the dataset including observations that lack birth months). Although twins are sometimes explicitly indicated in the data sources, this is not always the case. Thus, for the sake of consistency across our populations, twin births were identified when at least two individuals born to the same mother appeared with similar birth dates, according to strict criteria: if the exact birth dates were available, then offspring were identified as twins if their birth dates were no more than one day apart. If the exact birth dates were not available, then an identical birth year and month were considered sufficient for positive twin identification.
Analyses and simulations Characterisation of the relationship between twinning and fertility. We began by characterising the relationship between lifetime twinning status and maternal total births by fitting two models. For the first, we used a Generalised Linear Mixedeffects Model (GLMM) to investigate whether the mothers of twins (twinners) had experienced a larger or smaller number of births than mothers who only had singletons (non-twinners). We fitted this GLMM on the mother-level data with the R package spaMM  63  using the call: fitme births total $ 1 þ twinner þ ð1jpopÞ; À data ¼ mother level data; family ¼ Tnegbinðlink ¼ "log"Þ Á ð1Þ The response variable births_total refers to all births recorded over a mother's observed lifetime (count data). The term 1 informs the function to fit an intercept (which happens by default, but is indicated here for clarity).
1a , b and Supplementary Tables  1, 2. While models 1 and 2 represent two sides of the same coin, the fit of both models is justified because each model formulation provides complementary information: expressing the effect of twinning on fertility relates to previous work  [5] [6] [7] [8] [9] [10] 57  and expressing the effect of fertility on twinning is a first step toward identifying what shapes twinning propensity, the focus of this paper. For the model predicting total births (model 1), we chose to use a negative binomial error structure. Using this error structure produced a fit of the data that was better than a (truncated) Poisson-the usual alternative for count data-as evidenced by much smaller marginal and conditional AIC values  64. Here we specifically used a truncated negative binomial distribution because the data do not possess zeros by construction (only mothers are present in the dataset, i.
e. back-transformed from the scale of the linear predictor) and conditional on the fixed and random effects. Unlike the traditional conditional predictions computed for a specific value of the random effects (often 0), such computation provides unbiased predictions and should be favoured in the context of GLMMs where random effects act nonadditively on the expected response (which is the case when the link function of the model is not identity  66  ). We estimated 95% intervals for these marginal predictions (CI 95% ) using parametric bootstraps with the help of the function spaMM_boot() from the R package spaMM and boot. ci() from the R package boot. More details can be found by looking at the code of the functions compute_predictions() and compare_prediction() in our supporting R package twinR (see Code availability).
Simulating the life history of mothers. We produced an individual-based simulation model of human female life history to investigate the contribution of four mechanisms to the relationship, shown in Fig. 2 , between per-birth twinning probability and maternal total births-an approach generally known as patternoriented modelling  67. Each simulation proceeds in the following way: first, we initialised the simulation with representations of the exact same mothers present in the observed dataset, setting their population and maternal identities as the real ones, their starting ages at the observed values for age at first birth and their parity to one. Following this initialisation, the virtual lives of mothers proceeded as multiple iterations of a sequence of three life-history events, informed by statistical models (see below) and subject to the hypothesis being tested (Supplementary Figs.
Testing candidate mechanisms using simulations. To test how each mechanism or association of mechanisms influenced the relationship between twinning and fertility, we ran simulations under each possible set of activated or inactivated mechanisms. We tested all possible sets and we thus built a total of 4 2 = 16 simulation scenarios (Supplementary Figs. 3, 4 ). For each simulation scenario, we ran simulation replicates (see Supplementary Notes for details and information on the numbers of replicates), then fitted model 3 on the dataset produced by each replicate and extracted the estimate for the slope associated with the term births_total in that model (β*). We then consider, in turn, that each simulation scenario may have generated the data. Each simulation scenario is thus considered as a null hypothesis which we aim at testing.
For each set of replicates, we extracted the twinning rate, the twinner rate, the mean number of offspring produced and the mean total number of births. We report the means of these metrics, as well as the 95% Central Range from simulation replicates (CR 95% ), which we directly computed by extracting the corresponding quantiles from the distribution generated by the replicates. Realism of the simulations. We checked that the simulated life history closely matched that of the real mothers represented in our dataset beyond what is captured by the relationship between twinning and fertility. To do so, we compared different metrics related to fertility and twinning between the real and simulated data. We chose to perform this comparison under the simulation scenario PIS since it produces the best goodness-of-fit.
The results of this quality check confirm that our simulations represent the reproductive lives of the mothers appropriately (Supplementary Fig. 6 ). Studying the effect of mortality on the number of offspring using simulations. To account for the fact that not all offspring have the same expected survival, we also applied a survival weight to each simulated offspring before averaging the numbers for a given simulation set (baseline twinning propensity or enhanced, see Results). We used as weights the estimates for the probability of offspring survival between birth and adulthood provided by two publications associated with some of the data we used there. Specifically, following Helle et al. 8  , we used a weight of 0. 603 for twins, 0. 838 for singletons from twinners and 0. 815 for singletons from nontwinners.
Alternatively, following Haukioja et al. 3  , we used a weight of 0. 337 for twins and 0. 706 for singletons from all mothers. Implementation details. All statistical analyses were performed in R version 4. 1  70. The main R packages we used were spaMM 63 version 3. 9. 40 for the fit of all the statistical models, boot  71, 72  version 1. 3-28 for the computation of confidence intervals based on parametric bootstraps, and R6 73 version 2. 5. 1 for defining the object used to run the simulations. The DESCRIPTION file from our package twinR (see Code availability) lists the additional R packages required for this project (e. g. those used for plotting and data manipulation). tools; F. R. drafted the Supplementary Notes; A. C. and I. J. R. wrote the main text; E. P. , F. R. and V. L. edited draft versions of this text; and C.
Acknowledgements The authors thank all the people, too numerous to name, who contributed to generating the datasets used in this study. We also thank  Jean Michel Gaillard ,  Ruth Mace , and  Olivia Judson  for comments on our manuscript. I. J. R. was supported by  Durham University  and the  German Academic Exchange Service (DAAD). V. L. was supported by the  Academy of Finland. I. J. R. , A. C. and V. L. also thank the  Wissenschaftskolleg zu Berlin  which hosted these authors during part of this research. Simulations were run on the DZG server provided by the  Leibniz IZW ; on the MESO@LR platform, financed by the  Occitanie Region ,  Montpellier Mediterranean Metropole  and the  University of Montpellier ; and on the  Montpellier Bioinformatics Biodiversity  platform supported by the  LabEx CeMEB , an  ANR  " Investissements d'avenir" program  ( ANR-10-LABX-04-01 ).