dataesr/scientific-paragraphs-categorization huggingface dataset:
Livestock manure spread We estimated, at the cantonal scale, the total amount of LM spread on the UAA by source (herbivore vs. granivore), by type of production (solid vs. liquid), and by destination (crops vs. grasslands). A database from ADEME (2007), the French Ecological Transition Agency, contains estimates of LM spreading at the departmental scale, but this scale is too large to capture the relation between LM spreading and SOC content  (Meersmans et al. 2012). Although the number of livestock units is available at the cantonal scale, it seems to have no statistically significant relation with SOC content, likely due to differences between herbivore and granivore manure  (Paroissien et al. in review). Thus, the amount of LM spread at the cantonal scale must be estimated as a function of the types of livestock present.
We also distinguished the type of LM produced (solid vs. liquid) and its destination (crops vs. grasslands). We expressed the amount of LM spread in terms of its dry matter content to obtain a total amount of LM spread per canton, which we ultimately converted to a mean amount of LM spread per hectare on crop and grassland areas. To estimate the amount of LM produced by herbivores and granivores, we used data from the 2000 and 2010 French agricultural censuses. The difference between the LM produced and the LM spread is given by the French program to control pollution of agricultural origin  (PMPOA 2007). The number of herbivores corresponds to the number of dairy and suckler cows, other cattle, ewes, other ovines, goats, and other caprines. In the calculation of LM of herbivores, livestock units are based on nutritional requirements of each type of herbivore (definition of IDELE) and are used as coefficients for the different types of herbivores.
The liquid waste mentioned in the 2000 agricultural census corresponds to organic waste from milk processing (washing water from animal exploitation facilities and equipment on the farm). All LM from sows and fattening pigs was liquid, and they were assumed to be present on the farm throughout the year  (PMPOA 2007). For poultry, we assumed that 60% of the LM was solid and 40% was liquid in each canton. Total LM equaled total LM of herbivores and total LM of granivores. The LM produced by herbivores was estimated for each canton as follows: LM herbivores ¼ LM herbivores solid þ LM herbivores liquid ð1Þ where LM The LM produced by granivores was calculated for each canton as follows: To estimate the LM spread per hectare and per land-use type, we distinguished the destination (crops vs. grasslands) by using two additional datasets: the surface area on which LM was spread (SAMS), which is available at the regional scale for livestock farms of several economic and technical orientations (e.
2014 ). This database is the output of a national French program that aims at storing results of soil tests commissioned mainly by farmers to manage the fertilization of their plots. The results were provided by ca. 30 soil-testing laboratories certified by the French Ministry of Agriculture, all of which applied the same analytical standards. This database stores soil test results from more than 2. 6 million agricultural plots spread over the French mainland and sampled from 1990 to 2014. The sampling depth corresponded on average to 0 and 25 cm for crop soils and 5 to 15 cm for grassland soils  (Schvartz et al. 1997 ). To respect the confidentiality of the plot owner, the sampling location is not perfectly known and corresponds to the area of the municipality. The sampling resolutions in space and time are heterogeneous as the sampling protocol is not controlled.
These constraints are factored into the statistical analyses by computing summary statistics of the soil test results per spatio-temporal entities. As already presented, we pooled the data in space per canton as this resolution matched the one of the LM spread data. In time, we pooled the data per 5-year period and we focused on two time-periods-2000-2004 and 2010-2014-since SOC dynamics involve slow processes. Moreover, these periods matched the date of the census campaigns of the LM spread data  (2000 and 2010). We retained in this study information about SOC content (gC/kg) and soil texture (i. e. , clay, loam, sand). We computed for every spatio-temporal entity, the mean, the first and last decile of these soil indicators. The SOC changes were computed as described in  Saby et al (2017).
A resampling technique is proposed to consider the possible bias in the database. The sampling protocol associated with the data collection procedure of the soil data may be referred to as a purposive sampling design. This type of design does not guarantee getting unbiased estimates of the parameters of the statistical distributions  (De Gruijter et al. 2006 ). However, we assumed in this study that the bias could be reduced if the number of observations per canton is large enough, with at least 50 soil test results. However, given high variability in the proportion of UAA in the area of French cantons, the amount of soil tests had to be a function of sampling density per hectare UAA rather than of the number of analyses per canton. The minimum sampling density of soil test results was set at 50 soil test results divided by the average percentage of the UAA of French cantons in the 2000 and 2010 agricultural censuses.
This corresponded to an average of one soil analysis sample per 145 ha. Using this resampling protocol, we have selected cantons for which we observed this minimum density of soil test results for 2000 and 2010. Finally, we had two sets of data: one dataset where we selected cantons for which data were available; one subdataset where we selected cantons with a minimum sampling density of soil test results (1 soil test per 145 ha) (see Fig. 2 ).
Climatic data As SOC dynamics is impacted by temperature and moisture  (Chen et al. 2019 ), we used a set of climatic indicators based on annual statistics at the cantonal scale from 1970 to 2010 obtained from  Joly et al. (2010). We retained two sets of indicators. The first one corresponds to the absolute value of different indicators: number of hot days (above 30 °C), inter-annual variability in temperatures in July (the SD of the 30 monthly temperature values during the reference normal), and inter-annual variability in monthly precipitation in July and January (the SD of the 30 monthly precipitation values during the reference normal).
-Minimization of the Akaike information criterion (AIC) to select the most parsimonious model. We used the general least square approach rather than a machine learning approach in order to explain the relationships between the explanatory variables and the explained variables, with information about the statistical significance and the signs of the relationships, while machine learning algorithms are better suited to provide accurate predictions  (Wadoux et al. 2020). We have standardized the variables to ease the comparison and the interpretation of the statistical analysis. Statistical analyses were performed using R software (v. 3. 5. 1) (R Core Team 2018) and the interface Rstudio (v. 1. 3. 1073). To conduct the linear regressions, we used the packages nlme (v. 3. 1-149; Pinheiro et al.
" This step represented a sort of sensitivity analysis where we tested whether the impacts of livestock farming variables on SOC content and dynamics vary depending on different initial levels of topsoil quality. Mean weather and soil texture data were used and assumed to remain constant over time. Regression 1: SOC content in period 2010-2014 ¼ ∝ i control variables þ β i UAA land use variables þ δ 1 SAMS from herbivores on cereals þ δ 2 SAMS from herbivores on grasslands þ δ 3 SAMS from granivores on cereals þ δ 4 SAMS from granivores on grasslands þ δ 5 total LM from herbivores þ ε ð15Þ Regression 2: SOC content in period 2010-2014 ¼ ∝ i control variables þ β i UAA land use variables þ δ 1 total LM from herbivores þ δ 2 total LM from granivores þ ε ð16Þ 2. 7 Impact of LM spreading on the change in SOC content from 2000 to 2010 Two regressions of change in SOC content from the period 2000-2004 to the period 2010-2014 as a function of LM spreading were performed (Eq.
4, 5, 6, and 7). Unlike previous studies based on BDAT data  (Issanchou 2017; Paroissien et al. in review) , other studies have found a negative impact of maize area on SOC content  (Loveland and Webb 2003). Currently, when grasslands are converted, they are usually converted into maize  (Dumont et al. 2019) , which corresponds to an intensification of livestock farming. When soil from a maize field is analyzed and has a high SOC content, it is usually the impact of the preceding grassland that is measured. In addition, herbivore manure is more likely to be spread on forage maize than on other crops. The cantonal proportion of permanent grasslands had a positive impact on SOC content and dynamics (Figs. 4, 5, 6,  and 7 ); however, the change in the proportion of permanent grasslands did not have a significant impact on that in SOC (Figs.
Bias and interest of the databases used The results must be interpreted in light of the limits of the databases used in our analysis. The soil test results in BDAT are requested by farmers, usually to manage their fertilization and the general conditions of their fields  (Saby et al. 2008 ). These samples come mainly from crop soils with high production potentials rather than grassland soils. Furthermore, the distribution of the samples varies in time and space  (Saby et al. 2014). The commercial policies of soil analysis laboratories and agricultural advisors influence the soil analysis chosen and the plot sampled  (Lemercier et al. 2006). The data used here, which are calculated summary statistics by canton and for 5-year periods, limited this potential bias. The impact of spreading granivore manure on crops must be considered carefully.
The change in the number of granivores was calculated from the agricultural censuses of 2000 and 2010. Although we assumed that the change in pigs and poultry was continuous, their numbers vary according to demand (especially for poultry), which may have biased analysis and interpretation. In contrast, numbers of cattle are more stable in time and have slower dynamics. More cantonal data relative to spreading manure practices and livestock could allow us to better explain the local trends in terms of SOC dynamics that can be masked on a larger scale. There are also omitted variables that may impact SOC content that we did not consider here, such as sludge from plants treating waste waters or long-distance exchanges of organic matter. Such variables may have an impact on SOC measures, especially in areas with low SOC content.
Despite these limits, the databases used allowed us to perform statistical analysis at the national scale in 2365 cantons. At the national scale, spreading of LM influenced SOC content and dynamics significantly. Our results show the importance of the nature of the manure: solid manure increased SOC content, unlike liquid manure. Spreading herbivore manure on crops increased SOC content, but spreading granivore manure decreased it. These results could be consolidated using updated data from the 2020 French agricultural census and 2015-2019 BDAT data.
Conclusion The objective of the study was to improve understanding of impacts of livestock farming on soils at the national scale in France. Our results suggest that livestock farming has an important role in maintaining and increasing soil organic carbon content, through how manure is managed. Our results, obtained for 2365 French cantons for a 10-year period, confirmed previous results of livestock manure spreading on soil organic carbon content and dynamics, in particular the positive influence of spreading herbivore manure. These results confirm those of previous studies at smaller geographical scales. Spreading herbivore manure on crop areas increases soil organic carbon content, but spreading granivore manure appears to decrease it. Livestock manure spread on grasslands has no significant impact on soil organic carbon content, likely due to under-representation of grassland soils in the French soil test database.
The nature of the manure is important to consider. Whether livestock manure is applied in solid or liquid form can have opposite effects on soil organic carbon content: spreading solid livestock manure increases soil organic carbon content, while spreading liquid livestock manure may decrease it. These results demonstrate the importance of the complementarity between crop and livestock as a mechanism to maintain soil ecosystem services, including soil fertility and carbon sequestration. Our study demonstrates that soil data collected for a different purpose in the past can be used to study soil organic carbon dynamics that are relevant today. long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Acknowledgments The authors thank the other participants of the soil working group of the  GIS "Avenir Elevages" (Scientific Consortium for the Future of Livestock) , in particular  Pierre Mischler. The soil-test database was funded by the  French Ministry for Agriculture  in the framework of a Scientific Group of Interest, the  Groupement d'Interêt Scientifique Sol. We thank the soil-test laboratories participating in this program.
Search methods for identification of studies The electronic databases of EMBASE, MEDLINE and PsycINFO were searched from the date of database inception to July 19, 2020. To identify relevant studies, the following MeSH search terms and key words for optimism/pessimism were used: optimism OR optimistic OR "life orientation test" OR MMPI; pessimism OR pessimistic OR "life orientation test" OR MMPI; and for all-cause mortality: fatal* OR death* OR mortalit* OR "all-cause mortality" (refer to S1, S2 and S3). Reference lists of the included studies, plus any relevant review articles that were retrieved from the database search, were searched in order to identify any other potentially relevant studies. A subsequent search on August 5, 2020 identified one additional study.
Data collection and analysis Two review authors (HC and AZZP) independently screened the title and abstract of the studies from the search, followed by full-text screening. The first reviewer (HC) completed, and the second reviewer (AZZP) verified, the extracted information, using a form designed a priori for the purpose of this review. Any discrepancies were resolved through discussion. The following data were sought: author/year, country of publication, sample size, study design, baseline participant characteristics (age and gender), tool used to measure optimism, as well as how mortality was ascertained, follow-up time, confounders, and the main findings.
Funnel plots and Egger's test were used to assess heterogeneity or reporting bias  (Sterne et al. , 2011). Subgroup analyses were identified following data extraction and performed to determine whether associations differed according to the sample used in the study (population-based, population-based with adjustment for health status/conditions, or patient sample). All statistical analyses were undertaken using STATA software, version 16. 0 (StateCorpLP, College Station, TX, USA).
Each study's quality was assessed in a standardized way, and data were extracted according to a table designed a priori in a customized form. The findings of this systematic review indicate that the majority of studies researching optimism (59%) reported optimism being associated with lower risk of all-cause mortality. Greater unrealistic optimism and pessimism were associated with an increased mortality risk. Two out of the three studies reporting the association between both optimism and pessimism and mortality reported no association between optimism and mortality, while they observed that pessimism increased the risk of all-cause mortality. The results of this review support recent arguments that future research should study optimism and pessimism as distinct constructs. Optimism and pessimism are considered to be potentially modifiable determinants of health.
, Faratci et stroke patients specifie 22. 4 45 -59 15% Department of Registry, medical 1. 42) LOT tertile), HR health ethnicity, demographics, 2011) al. , 2017) patients surviving d (33. 9% of Health databases, records, death 1. 04 (0. 78, 1. 38) cardiovascular smoking, health (S. J. Lee USA Prospective, myocardial 313 * 6 'optimistic' sample), 60- Not specified Social Security certificates, 2 questions: 'I am 2 months post (middle tertile), age, sex, type of disease status, status (including et al. , 2003) patients infarction months median 47 74 (37. 8% of Death Index physicians and optimistic that my transplant: RR HR 0. 67 (0. 47, transplantation diabetes, obesity), undergoing (19 -65); sample), family members transplant will go 0. 45 (0. 22, 0. 92) 0. 95) (upper procedure, Graft depression perceived social 'less well' & 'if p =0.
Data and methods This work uses the Urban Atlas 2012 dataset developed by the Copernicus land monitoring services, which provides a precise description of land use for the nearly 700 main European cities in 2012, defined at the level of functional urban areas (FUAs, formerly denominated Larger Urban Zones LUZ). We study here, in their 2012 version, the 300 urban areas which are available both in the 2006 and 2012 versions of the Urban Atlas. Land use change in those cities between 2006 and 2012 is rather small  15. We combine this dataset with the 1 km 2 population grid provided by Geostat for 2011, which covers the whole European Union. We use this population grid essentially to compute the total population of each city, as the focus of this work is mainly on land use. Our analysis is radial, studying intra-urban phenomena as functions of the distance to the city center, which is chosen as the location of the (main or historical) city hall.
In doing so, we try to follow a parsimony principle. We know that more complex models with more parameters have higher performance in terms of the goodness-of-fit, in the sense that the model is closer to the data points. However, the results of the model, which are the fitted parameters, are more difficult to study and understand: the information contained in the model is diluted among the different parameters. This can be related to the problem of overfitting, which is well-known in computer science and machine learning. Physicists usually express this problem using von Neumann's famous quote about fitting an elephant  16. Here we aim as a consequence to use the simplest possible model, with the least possible number of parameters but still a correct description of the dataset. The exercise to be carried out here can be stated simply.
We have 300 artificial land use radial profiles s N (r) , one for each city of the dataset. As illustrated on examples in Supplementary Fig. 1 , these profiles are roughly exponential, so that they look like straight lines on a semi-log graph. We want to fit them with an appropriate model respecting a parsimony principle. Our first choice considering the shape of these curves is a simple exponential fit for each city. There are however two (main) ways to carry out this exponential fit: to do a linear fit (which we denote L) of the logarithm, or to do a non-linear fit (which we denote NL) of the raw value, with a non-linear least squares fitting method. where a N is the share of artificial land in the center and l N is the characteristic distance of exponential decrease, at which the model estimates that exp(-1) ≃ 37% of the land is artificial.
The peculiarity of coastal cities is captured by the only parameter of the SNL model, rather than being spread over the two parameters of the NL model. This shows a clear advantage of the one-parameter model: all the information extracted from the data is contained in only one parameter. Next, we test the two models on continental cities only, and present the results in Table  1 : we select only cities for which the share of missing land within a rescaled radius r � = 15 km of the center is less than 20%, which means 237 cities out of the original 293. In this case, we call NL20 and SNL20 the log-log fits of the characteristic decrease distance l N with respect to total population N, for the two-and one-parameter models respectively. NL fit NL fit 0. 007xN 1/2 0. 007xN 1/2 0. 007xN 1/2 0. 007xN 1/2 0.
This suggests that coastal cities are indeed special cases, which do not have a clearly exponential profile due to the land covered by water, and that the one-parameter (SNL) model detects them better. In addition, while this model is clearly more parsimonious, its description of the data is almost on par with the two-parameter model. As it also has the advantage of not overestimating the artificial share of land in the center a N , we prefer it to the two-parameter model which overestimates it for some cities (where a N > 100%).
This index gives a characteristic population density in the city's artificial core. If l N is measured in kilometers, our analysis shows that this city mass index would have on average a value of ⟨N∕l 2 N ⟩ ≃ 1∕l 2 1 ≃ 20, 000 inhabitants/km 2 in our European data- set. Higher values would correspond to compact cities, while lower values would indicate sprawled cities (not forgetting that coastal cities need to be treated separately). Actually, we prefer to use the inverse indicator l 2 N ∕N , which has more intuitive variations. It corresponds to the characteristic artificial area per capita, and would give on average l 2 1 ≃ 50 m 2 in our dataset. Higher values indicate sprawled cities while lower values would indicate compact ones. This illustrates again the fact that the parameter l 1 provides at the same time a characteristic scale of artificial area per capita and of population density.
Acknowledgments This study was partially supported by the Deutsche Forschungsgemeinschaft (German Research Foundation) under project LU1174/8-1 (PACOG) of the research unit FOR1898, and under SPP 1788 (CoSIP) project CH1482/3-1 (CS-PMSE-MIMO). R. Marino acknowledges support from the project "EVENTFUL" (ANR-20-CE30-0011), funded by the French "Agence Nationale de la Recherche"-ANR through the program AAPG-2020. The authors would like to thank Nikoloz Gudadze for suggesting us to look at the PMSE observations from July 16, 2016, and Dr. Miguel Larsen for his valuable suggestions. Open access funding enabled and organized by Projekt DEAL.
Leg 2 CTD data obtained along the four transects parallel to the glider tracks (Figure  1C ) were used to compute DH at 30 m depth. In Leg 2, CTD casts sampled the water column down to 500 m depth, and the reference level for the inference of the DH was set to 500 dbar. The DH calculated along the four CTD transects was compared to that computed from the Slocum glider during the return transect, also referenced to 500 dbar for consistency. The DH calculated from glider data is compared to the ADT obtained from both the Sentinel-3A dataset and the L4 altimetric gridded product. Prior to the comparison, a low-pass Loess filter  (Cleveland and Devlin, 1988)  with a cutoff of 30 km was applied to the glider, CTD and Sentinel-3A datasets in order to remove measurement noise and small-scale variability (see e.