dataesr/scientific-paragraphs-categorization huggingface dataset:
For the second component of the conditional model, GzLMs with a Poisson distribution and a log link function were applied on a zero-truncated dataset. Because there are nine ecological comparisons on the same data (i. e. , Relationships #1a, #1b, #1c, #2, #3, #4, #5a, #5b, and #6), a Bonferroni correction of the alpha level of 0. 05 divided by nine was applied. To further examine Relationship #2, i. e. , the relationship between Distance and Degradation, the data were partitioned into five size-classes of mussel shell lengths: ≤1. 0, >1. 0 to 2. 0, >2. 0 to 3. 0, >3. 0 to 4. 0, and >4. 0 cm. A partitioned GzLM using a Poisson distribution and a log link function was performed on the dataset for each of the five size-classes. Similarly, the data were partitioned into four size classes of shell lengths: >1.
Relationship #2: distance from range edge and shell degradation Shell degradation (an estimate for percent cover of endolithic infestation; Degradation) weakly decreased with increasing distance from the warm-edge range limit (Distance; Table  2 ). Moreover, Distance only explained about 3 % of the variation in Degradation (Table  2 ). After partitioning the dataset into five size-classes of shell length, the relationship between Distance and Degradation was found to be significant for the >1. 0 to 2. 0 and the >2. 0 to 3. 0 cm size-classes (i. e. , intermediate sizes; Fig. 5 ) but not for the small, ≤1. 0 cm, or large, >3. 0 to 4. 0 and >4. 0 cm, size-classes of mussels (Table  3 ). Despite being a significant predictor, even for the two intermediate size classes, Distance only explained about 2 % of variation in Degradation (Table  3 ).
Relationship #4: distance from range edge and barnacle epibionts The occurrence of barnacle epibionts on mussel shells (presence-absence Epibiosis) increased weakly with distance from the warm-edge range limit (Distance), as did the zero-truncated abundance of epibionts (zero-truncated Epibiosis; Table  2  and Fig. 7 ). Overall, Distance only explained about 1 % of the variation in the occurrence of epibiosis on shells and <1 % of the variation in the abundance of epibionts (Table  2 ). After partitioning the dataset into four size-classes of shell lengths, a positive relationship between Distance and presence-absence Epibiosis was observed Distance from range edge (km) Shell degradation index Fig. 5. Relationship between distance from warm-edge range limit in South Africa and mean (± SE) shell degradation index value in two intermediate size-classes of Mytilus galloprovincialis (>1.
0 cm in shell length). Consequently, we removed shell lengths that were >4. 0 cm from the dataset and re-evaluated the relationship between Length and Epibiosis (abundance and densities) with post hoc twocomponent conditional models on the subset of data (≤4. 0 cm in shell length). Bonferroni correction (alpha level of 0. 05 divided by two) was applied, because there were two post hoc tests on the same data. For these post hoc models, the occurrence (presence-absence data), abundance of epibionts (zero-truncated data), and estimated densities of epibionts (zero-truncated data) all increased with increasing Length (Table  A3  and Fig. A5  in the Appendix), which was consistent with the initial conditional models.
8 ). Degradation explained about 13 % of both the variation in the occurrence of epibiosis on shells and the variation in the abundance of epibionts (Table  2 ). After partitioning the dataset into four size-classes of shell lengths, a positive relationship between Degradation and the occurrence of epibionts (presence-absence data) was observed for the >1. 0 to 2. 0 (deviance explained: 5 %), >2. 0 to 3. 0 (2 %), and >3. 0 to 4. 0 cm (3 %) size-classes of mussels, but with no relationship for the largest sizes (i. e. , >4. 0 cm; Table  5 ). For the second component of the models, a positive relationship between Degradation and the abundance of epibionts (zero-truncated data) was found for each of the four size-classes of mussels (deviance explained ranged from 4 to 10 % depending on the size-class; Table  5 ).
To evaluate the importance of the thermal gradient in structuring and maintaining EF across our study area, we gathered evidence from two different sources: interpolated inshore data from the literature and data from loggers installed on intertidal rocks. Effective shore level (ESL) estimations have been developed to delineate periods of emersion and immersion  (Harley and Helmuth, 2003; Gilman et al. , 2006)  and have been widely applied in intertidal ecological studies (e. g. ,  Lathlean et al. , 2011; Helmuth et al. , 2016; Monaco and McQuaid, 2019; Monaco et al. , 2019; Monsinjon et al. , 2021). The identification of sudden drops in temperature, revealing the moment when data loggers are immersed in cooler seawater, is sensitive to the temperature threshold that is applied. If the magnitude of the threshold is too low, false positives may be detected (e.
, 1998; Bownes and McQuaid, 2009; Pfaff et al. , 2011; Reaugh-Flower et al. , 2011; Radloff et al. , 2021). In the present study, sampling occurred throughout the year, which could have introduced geographic variability in the abundance of small-sized individuals into our dataset. To address this, the relationship was re-assessed after settlers and recruits (i. e. , <1. 0 cm in length) were removed from the analysis. This revealed a similarly significant relationship between mussel size and distance from its range edge, although only 1-2 % of the variability in mussel size could be explained by distance from range edge, our proxy for EF associated with a thermal stress gradient. This suggests that processes other than EF associated with abiotic gradients (e. g. , time since colonisation, biotic interactions with other species) likely play a larger role in controlling growth rates, recruitment, and size-structure in M.
Acknowledgements We would like to express our thanks to  Jaqueline A. Trassierra  and  Molline N. C. Gusha  for their assistance in the field and to  Cassandra Barker  for providing in situ temperature data. Our thanks also to the  South African Navy Hydrographic Office  for the 2019 and 2020 hourly tidal predictions. We are indebted to  Ticia Swanepoel  for administrative support. KCKM conceptualised the study, analyzed the data, and prepared the manuscript with intellectual and editorial input from PWF and CDM. In addition to collecting mussel samples and editorial input, JRM developed the modified method to estimate effective shore level. Collection permits (RES2019/ 30, RES2020/73) for marine species were issued to the  Department of Zoology and Entomology, Rhodes University , by the  South African Department of Environmental Affairs, Forestry, and Fisheries (formerly the Department of Agriculture, Forestry, and Fisheries).
Data availability Data and novel code generated from the present study are deposited online in Dryad and available at https://doi. org/10. 5061/dryad. h70rxwdmn https://datadryad. org/stash/share/LEfX9e9p_Tb7bu_ Ke5wQzAyMVD1uLNLy1OZ-cWl0PX0.
We collected functional attributes and ecological indicator values in the following databases: Ecoflora  [30] , Baseflor  [31]  and LEDA  [32]. We produced a functional tree from the 'species × traits' matrix using the 'gawdis' R function, which avoids a disproportional contribution of categorical traits  [33]. Functional α-diversity is the total branch length of the functional tree linking all species present in each community  [34]. On the other hand, functional β-diversity is the total length of branches that are unique to each community. We computed diversity indices with the package BAT  [35]. Pairwise correlations and distributions of trait values are given in electronic supplementary material, figure  S4.
All VIF values were lower than 3, indicating that collinearity was not an issue  [38]. Note that we considered using generalized additive mixed models (GAMMs) to incorporate landscape categories (A, B and C) as a random effect on the intercept. However, Akaike information criterion (AIC) was consistently higher and adjusted R² lower for GAMMs, indicating that landscape category was not relevant (taxonomic α-diversity: AIC GAMM = 1928 versus AIC GAM = 1883, R 2 GAMM ¼ 0:24 versus R 2 GAM ¼ 0:35/functional α-diversity; AIC GAMM = 707 versus AIC GAM = 672, R 2 GAMM ¼ 0:21 versus R 2 GAM ¼ 0:34). We computed Gaussian GAM(M)s using the R package mgcv  [39]. Second, we used generalized dissimilarity modelling (GDM) to assess the effects of field margin structure and long-term management on taxonomic and functional β-diversity.
GDM is an extension of matrix regression that allows the incorporation of several explanatory variables, including geographical distance, to predict β-diversity  [40]. We fitted models using the R package gdm with the default number of splines and knots  [41]. We used the R function gdm. varImp to assess model significance and variable importance, using matrix permutation (n = 100) and backward elimination procedure. In addition, we estimated uncertainty (standard deviation) in the fitted splines using a bootstrapping approach, subsampling 80% of the dataset 100 times. Third, we used RLQ and fourth-corner analyses to assess the effects of field margin structure and long-term management on the functional composition of plant communities  [42]. Before these analyses, we removed rare species whose frequency of occurrence was lower than 2%, and we log-transformed species cover, as both very rare and very dominant species may unduly influence the results  [43].
The remaining species represented 98% of the total cover of herbaceous vegetation. We also log-transformed seed mass, which was highly skewed (electronic supplementary material, figure  S4 ). Then, we used a fourth-corner analysis to test the significance of correlations between each trait and each environmental variable. Further, we tested the correlations between RLQ axes and traits or environmental variables. For all tests, we used 49 999 random permutations of species and plots. We used the R package ade4 to perform these analyses  [44]. We considered incorporating landscape categories (A, B and C) using partial RLQ analysis  [45]. Again, landscape category did not seem relevant as the percentage of variance explained by the first two axes was lower for the partial RLQ analysis (89. 75 versus 93.
We also emphasize that we surveyed plant communities in 2015, which are most likely a subset of plant communities that were present before agricultural intensification began in the 1950s. Many local extinctions probably have already occurred, leaving mostly those species best able to cope with strong anthropogenic disturbances. In any contemporary study, we most likely underestimate the negative impacts of agricultural intensification on αand β-diversities, especially considering the existence of shifting baseline syndrome  [67]. For instance, Kempel et al. [68]  revealed thousands of local extinctions of rare and threatened species between 1960 and 2001 across many habitats, including hedgerows and herbaceous field margins. Once the damage is done, we are no longer able to accurately estimate (or even detect) the impact  [69] , unless historical reference data are available.
S3. Species names are abbreviated using EPPO codes-see electronic supplementary material table  S4  or EPPO Global Database (https://gd. eppo. int). royalsocietypublishing. org/journal/rspb Proc. R. Soc. B 290: 20222179 (c) A multi-faceted approach for more robust conservation and restoration decisions Our study revealed significant discrepancies between the drivers of αand β-diversities of plant communities in field margins, highlighting that both facets should be considered when making conservation and restoration decisions. In addition, we did not find many forms of antagonisms or synergies between the preservation of αversus β-diversity. Most drivers affecting α-diversity did not affect β-diversity, and vice versa. This implies that it is possible to promote both αand β-diversity by adopting different sets of management practices.
(d) A word of caution Although long-term survey of communities is essential, the absence of a decline in αor β-diversity over time does not reliably rule out ongoing biodiversity loss. Indeed, Alignier  [19]  showed that the overall β-diversity of plant communities in surveyed field margins did not decline between 1994 and 2015. Revisiting the same dataset, but this time assessing the drivers of β-diversity and functional composition, we showed that some field margin structures and long-term management practices are maintaining plant β-diversity and species of higher conservation value, whereas others are potentially leading to biotic homogenization by selecting for common weeds. Such information is essential for making better conservation and restoration decisions before it is too late.
Acknowledgements. The postdoctoral research of S. B. is financially supported by  La Fondation de France  ( DIVERSIT ' HAIES  project). The authors would like to thank  Elven Lanoë ,  Jean-Luc Roger  and  Gérard Savary  for their help in collecting floristic data in 2015, and  Dominique Volland  and  Christophe Codet  for conducting management practice observations. The authors would also like to thank the farmers who made this study possible.
Objectives The use of medications among pregnant women has been rising over the past few decades but the reporting of polypharmacy has been sporadic. The objective of this review is to identify literature reporting the prevalence of polypharmacy among pregnant women, the prevalence of multimorbidity in women taking multiple medications in pregnancy and associated effects on maternal and offspring outcomes. Design MEDLINE and Embase were searched from their inception to 14 September 2021 for interventional trials, observational studies and systematic reviews reporting on the prevalence of polypharmacy or the use of multiple medications in pregnancy were included. Data on prevalence of polypharmacy, prevalence of multimorbidity, combinations of medications and pregnancy and offspring outcomes were extracted.
Study selection and data extraction Study selection was conducted in two phases. In the first phase, title and abstracts were screened by two independent reviewers against the eligibility criteria (AA screened all papers, SIL, AS, AF, UA and ZW were the second reviewers). We retrieved full-text papers for all potentially eligible studies. In the second phase, full-text papers were assessed by two authors independently (AA and AA-L) against the eligibility criteria. For all eligible studies, two authors (AA and AA-L) independently extracted the data using a piloted data extraction form, and assessed the risk of bias. Discrepancies were reviewed and resolved by a third independent reviewer (ZW). Data items extracted included: purpose of the study, setting, recruitment, inclusion and exclusion criteria, participant demographics (age, ethnicity, parity, deprivation), definition of polypharmacy, prevalence of polypharmacy, classification system for grouping medications, list of health conditions, follow-up length, any secondary outcomes, funding and conflict of interest.
We used the Newcastle-Ottawa critical appraisal checklist for observational studies to assess risk of bias in the individual studies during the data extraction stage. 19 ore medications) and the setting (primary or secondary care). Given the heterogenous nature of the studies, statistical pooling and analysis was not possible. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist for reporting of systematic reviews has been followed (online supplemental appendix S2).
Study characteristics Table  1  shows the characteristics of the included studies. Studies were published between 1991 and 2020. The study populations ranged between 369 and 981 392. Six studies examined the prevalence of polypharmacy using administrative data, seven used surveys to collect self-reported medication use. One study used administrative data for prescription medications and self-report for the use of OTC medications. In seven studies, women were recruited from hospitals (either birth hospital or antenatal clinic). 4 6 21 22 26 28 29 In the other seven studies, participants were sampled from a national registry or population-based database (such as pharmacy records). 20  23-25  27 30 31 itchell et al reported results from two different cohorts: Birth Defect Study (BDS) and National Birth Defects Prevention Study (NBDPS).
Risk of bias within studies Most of the study cohorts were considered representative of the population they were sampling from. Most studies ascertained pregnancy status using hospital or pharmacy records or from birth registries, which were considered likely to be accurate. van Gelder et al and Schirm et al used a pharmacy database to identify all children born within a given timeframe. 20 31 Women of reproductive age living at the same address as the child were identified in the database and their prescription data was collected for the 270 days before the child's date of birth. There is a chance that women could have been misclassified as pregnant if the child was not living with their biological mother. As discussed above, seven studies relied solely on selfreported medication use to measure outcomes, introducing the potential for recall bias.
Strengths and limitations This systematic review has several important strengths. We developed a structured and substantial review of the literature, according to preplanned and comprehensive search terms with the help of a librarian, who is trained to undertake searches in large database repositories. Screening was conducted according to a rigorous inclusion and exclusion criteria, and we used two independent reviewers for data extraction to minimise bias. Two databases were searched: MEDLINE and Embase. We did not limit our search to studies published in the English language to minimise language bias, although specific databases in languages other than English were not included. There are limited studies specifically assessing polypharmacy in pregnancy. There is no consensus on the definition of polypharmacy and polypharmacy is often not explicitly defined in the studies.
ACKNOWLEDGMENTS AND FUNDING SOURCES This research was supported by the  Centre National de la Recherche Scientifique (CNRS)  and the  European Community (Human Brain  Project,  H2020-785907 ). E. S. acknowledges a PhD fellowship from the  École des Neurosciences de Paris (ENP)  and from the  Fondation pour la Recherche Médicale (FRM)  -grant  FDT202012010566  and the financial support from  La Fondation des Treilles.
Researchers have warned that the paradigm about who should measure air quality (AQ) in cities can change as low-cost commercial sensors for monitoring atmospheric composition gain global popularity. The new paradigm implies the expansion of the traditionally governmental responsibilities for AQ monitoring (to collect, interpret, and explain the data) to previously uninvolved actors. This study reports a first practical example of such changed AQ paradigm that occurred in a large industrial city of Krasnoyarsk (Russia). We describe how severe problems with urban AQ and a limited access to the AQ data from governmental sensors triggered decentralization of the AQ monitoring in the city. The decentralization is manifested by the fact that both governmental network and crowdfund-based activist AQ network, are being used for scientific and, to some extent, advisory purposes.
In this case, especially in low-to-mid income countries, public may have little or no access to data related to the conditions of the AQ  (Pinder et al. , 2019a (Pinder et al. , , 2019b)) , which would decrease the possibility of immediate evaluation of the environmental agenda's progress. Under these circumstances, the traditionally governmental function for monitoring urban AQ can be potentially eroded, whereas the resulting gap can be alleviated by third parties  (Wesseling et al. , 2019) , who can collect, interpret AQ data and giving public advice based upon this data due to low cost and accessibility of cheap AQ nodes. It is plausible that the change of conventional paradigm of urban AQ monitoring might occur in Siberia. Most Siberian cities were planned as industrial hubs  (Rodionova and Krejdenko, 2014)  and ultimately evolved in large industrial or production centers.
Data and methods We describe the data and methods in this section. Section 3 relies on the publicly available information about the governmental network for AQ monitoring in Krasnoyarsk from the Regional Ministry of Ecology (http://krasecology. ru/), regional meteorological services (http://m eteo. krasnoyarsk. ru) and regional government of Krasnoyarsk (htt p://www. mpr. krskstate. ru/nmy). Section 4 relies on the scientific literature, legal documents, and regular reports form the Ministry of Ecology and Resources. Moreover, we applied the analysis of Google trends, which had been shown as a relatively reliable tool for expressing the public interest in environment-related topics  (Nghiem et al. , 2016). We calculated the summarized internet search points from Google trends per year for the keyword combination 'Krasnoyarsk air' in Russian ("Воздух Красноярск").
The modern management decisions and the limits depend on the regulatory emission limits, listed in the online document (Russian Emission Limits, 2017). Since 2017, the PM2. 5 concentration has been monitored by BAM-1020 dust analyzers manufactured by Met One Instruments  (Tokarev et al. , 2020). Several studies have been published about AQ in Krasnoyarsk which used the data from these stations  (Badmaeva and Tsimmerman, 2015; Shaparev et al. , 2019; Yakubailik et al. , 2019; Chernykh et al. , 2020; Tokarev et al. , 2020)  but it is hard to find any information about the validation of these instruments. The Krasnoyarsk AQ network provides the data for research purposes, and, to inform the public. It also uploads the data on their website (http://krasecology. ru). There, a map with the short-term (1 day) SAQI estimates based on ground-based observations is available for the aforementioned species.
More comprehensive information about AQ is uploaded by the Krasnoyarsk AQ network is in the form of monthly reports to one of the websites of Krasnoyarsk Hydrometeorological (Hydromet) Services (http://meteo. krasnoyarsk. ru). By August 2022, the latest report is by some reason available only for January 2020. Based on the data from the Krasnoyarsk AQ network, the Regional Ministry of Ecology issues alerts under meteorological conditions, conductive for air pollution episodes (so-called "Black Sky" regime). The "Black Sky" was enacted by the ordinance N112 from 01. 03. 2011 by the Federal Agency for Environmental Control of Russia. The "Black Sky" regime signifies the adverse meteorological conditions favorable for trapping the air pollutants in the planetary boundary layer. Interestingly, according to some studies, the "Black Sky" regime implies the "adverse" wind direction (bringing air from a high emission area to the city), a lack of strong wind, haze and stable atmospheric inversion.
Due to this, the governmental stations upload the information about AQ the governmental website with one-time SAQI on the map whereas Fig. 3. A screenshot from https://waqi. info/ website which collects AQ information from AQ sensors around the world (made on 07. 02. 2021, 15:05 UTC). Data from the governmental monitoring is shown alongside the Nebo network data without any distinctive marks. Note that by summer 2022, the total number of stations, deployed by Nebo and various branches of government increased, but it is difficult to estimate the exact number of sensors. The full list of sensors we found in the open access by August 2022 is shown in the Supplementary material (Fig. S1 ). the air pollution is nearly always good. However, the same data uploaded to the aggregator waqi. info after conversion to EPA AQI somewhat indicate bad AQ during some days.