dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, a number of death reports closely associated in the registers were considered as grouped in space. They were identified as "grouped deaths" in this article. This was the case of multiple deaths in the same household and of deaths of two contiguous heads of households or of heads of households separated by a single spared household in the same street. The proximity relationships were based on the succession of households in the registers, and those deaths facing in the same street on the map (see below) and separated in the registers were not taken into account, as their positioning was in part arbitrary. We specifically analyzed those deaths of individuals grouped in space, who were more likely to have been victims of the epidemics. Data for the 6 "years of plague" and the previous 3 years are presented in Table  1.
Statistical analysis Our goal was to determine whether the deaths (cases) were randomly distributed throughout the city or whether they occurred as epidemic clusters and, that being the case, to localize the clusters and to determine whether the corresponding areas differed from the rest of the urban space in terms of death rate. The same approach was used for the analysis of grouped deaths (in this case, their distribution was analyzed by comparison with the addition of survivors and non-grouped deaths), of tax level categories and of professions. To this end we used Kulldorff's method  [22]  implemented in the SaTScan v. 9. 1. 1 software. The null hypothesis is that the observed prevalence follows a binomial distribution. The clusters are considered to be statistically significant if the relative risk values obtained by permutation at random are less than 5 percent higher or equal to those observed in reality.
1% as compared to 17% outside the cluster; chi-square test, p = 0. 04). Although of borderline statistical significance, this is in keeping with the previously mentioned data and suggests a spatial relationship between a higher proportion of low-tax payers and a lower death rate. We thus tested whether low-tax payers died differently from the other heads of households. To this aim, we took into account the register for the previous year. The 325 heads of household deceased in 1400 were recorded in the register for 1399 (the other 5 dead persons of 1400 were not heads of households). The same register mentioned 1,548 out of the 1,715 survivors of 1400 (the other 167 survivors were newly registered for 1400). In 1399, among this total of 1,873 heads of households, 893 were low-tax payers. On the following year, their death rate was 21% as compared to 14% for the 980 heads of households who were not low-tax payers (Table  4 ).
Acknowledgments Henri Labesse  ( Paris-Sorbonne-Paris 4 University ) designed the original program used for the database of the marcs tax registers. Françoise Vergnault-Belmont  ( École des Hautes Études en Sciences Sociales ) supervised the setting up of the medieval map. The  Maison des Sciences de l'Homme Claude-Nicolas Ledoux  kindly provided the SCAN251 files. Late professor  Henri Dubois  ( Paris-Sorbonne-Paris 4 University ) introduced AG to the marcs tax registers and continuously supported her work.
Abstract Processes that describe the distribution of vegetation and ecosystem succession after disturbance are an important component of dynamic global vegetation models (DGVMs). The vegetation dynamics module (ORC-VD) within the process-based ecosystem model ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) has not been updated and evaluated since many years and does not match the progress in modeling the rest of the physical and biogeochemical processes. Therefore, ORC-VD is known to produce unrealistic results. This study presents a new parameterization of ORC-VD for mid-to-high latitude regions in the Northern Hemisphere, including processes that influence the existence, mortality and competition between tree functional types. A new set of metrics is also proposed to quantify the performance of ORC-VD, using up to five different datasets of satellite land cover, forest biomass from remote sensing and inventories, a data-driven estimate of gross primary productivity (GPP) and two gridded datasets of soil organic carbon content.
The scoring of ORC-VD derived from these metrics integrates uncertainties in the observational datasets. This multi-dataset evaluation framework is a generic method that could be applied to the evaluation of other DGVM models. The results of the original ORC-VD published in 2005 for mid-to-high latitudes and of the new parameterization are evaluated against the above-described datasets. Significant improvements were found in the modeling of the distribution of tree functional types north of 40 • N. Three additional sensitivity runs were carried out to separate the impact of different processes or drivers on simulated vegetation distribution, including soil freezing which limits net primary production through soil moisture availability in the root zone, elevated CO 2 concentration since 1850, and the return frequency of cold climate extremes causing tree mortality during the spin-up phase of the model.
ORCHIDEE is the terrestrial surface component of the Institut Pierre Simon Laplace (IPSL) Earth system model. Since the first model description by  Krinner et al. (2005) , the representation of existing processes has been improved and new processes have been implemented, such as a physically-based multi-layer soil hydrology scheme  (de Rosnay et al. , 2002) , and a scheme describing soil freezing and its effects on root-zone soil moisture and soil thermodynamics  (Gouttevin et al. , 2012). These new parameterizations have been evaluated for static runs in which PFT maps were prescribed based on observed satellite land-cover information. Yet, their influence on the simulated PFT distribution when the vegetation dynamics module is activated has not been addressed. The original vegetation dynamics module in ORCHIDEE (hereafter "ORC-Figures.
The evaluation methodology developed here could be used for other DGVMs as well, and is thus of general interest for the DGVM modeling community. We present a new parameterization of vegetation dynamics in the ORCHIDEE High Latitude version (ORC-HL) described by  Gouttevin et al. (2012) , with modifications to the equations and parameters describing tree mortality, thermal constraints and a calibration of v cmax /j max (Sect. 2. 2). The results of the original module (ORC-HL-OVD) and of the new parameterization (ORC-HL-NVD) are evaluated against different satellite land-cover products, forest inventory data for forest area and biomass, and datadriven GPP and soil carbon products (Sects. 4 and 5). Because the biogeochemical and physical processes that characterize high latitudes interact in a complex way with the processes that control vegetation structure, in Sect.
ORCHIDEE High Latitude ORCHIDEE consists of two main modules: SECHIBA (the surface-vegetationatmosphere transfer scheme) which simulates energy and water exchanges between the atmosphere and land surface at a half-hourly time-step, as well as photosynthesis based on enzyme kinetics  (Ducoudré et al. , 1993; de Rosnay and Polcher, 1998) , and STOMATE (Saclay Toulouse Orsay Model for the Analysis of Terrestrial Ecosystems, Viovy, 1997) which simulates carbon dynamics at a daily time-step, including carbon allocation, biomass accumulation, litter and soil carbon decomposition, and phenology. STOMATE includes a dynamic vegetation module with equations adapted from the LPJ model  (Sitch et al. , 2003)  as described by  Krinner et al. (2005). ORCHIDEE High Latitude version (ORC-HL) is an evolution of ORCHIDEE including additional high latitude processes, described by  Gouttevin et al.
Code availability The ORCHIDEE model used as a starting point in this study is ORCHIDEE rev1322. The source code can be obtained at http://forge. ipsl. jussieu. fr/orchidee/browser/ branches/ORCHIDEE-MICT/ORCHIDEE?rev=1322. A detailed documentation and the forcing data needed to drive ORCHIDEE can be found at http://forge. ipsl. jussieu. Figures.
Simulation protocol Six different runs with ORC-HL (Table  2 ) were performed to test the impact of the new dynamic vegetation parameterizations and parameter calibrations. Since the modifications in vegetation dynamics module were mainly for temperate and boreal PFTs, the simulation domain is Northern Hemisphere from 20 to 90 forced by historical CRU-NCEP climate and rising CO 2 concentration. No climate data were available before 1901, so for that period, randomly selected years between 1901 and 1920 were used. The OLD run used the original vegetation dynamics equations from  Krinner et al. (2005)  in the ORC-HL version so that comparing NEW and OLD allows us to evaluate the improvements listed. The other four runs (EXP1-3, STAT) were similar to NEW except for one different setting for each run (Table  2 ).
Evaluation datasets We use satellite observations of land cover translated into the PFTs of ORCHIDEE to evaluate the simulated vegetation distribution. In order to account for uncertainties of observations, we used three different land-cover maps: the ESA CCI land cover v1. 1 for year 2010, GLC2000  (JRC, 2003)  and ISLSCP II vegetation continuous field for 1992-1993  (Defries and Hansen, 2009). The first two land-cover products (hereafter "ESA" and "GLC") were converted from their original classifications (22 categories based on LCCS system) into PFT maps, using the cross-walking method of  Poulter et al. (2011). The third product (hereafter "VCF") provides the fractional cover of bare ground, herbaceous vegetation and forest (further split into evergreen or deciduous, and broadleaf or needleleaf), and was merged with climate zones of the Köppen-Geiger classification system to resolve to PFT classes, based on  Poulter et al.
(2011). For Siberia, two additional regional land-cover maps were used, the PFT map of Siberia at 1 km scale from  Ottlé et al. (2013)  based on the GlobCover2005 product  (Bicheron et al. , 2006) , hereafter "OSIB", and the Russian land-cover dataset produced by International Institute for Applied Systems Analysis  (Schepaschenko et al. , 2011) , hereafter "IIASA", which was converted into PFT map using the cross-walking method of  Poulter et al. (2011). Along with ESA, GLC and VCF, the five land-cover products were used to evaluate the model skill at simulating the vegetation distribution across Siberia. The PFT maps were aggregated at 2 • × 2 • , matching the resolution run by ORCHIDEE in this study. Figure  2  displays an RGB composite-color map of the vegetation fractional cover partitioned between broadleaf (including evergreen and deciduous, red), needleleaf evergreen (green), and needleleaf deciduous (blue) trees, from the five PFT maps.
Simulated GPP was evaluated using the data-derived field obtained from FLUXNET data, satellite fAPAR and gridded climate and land-cover data using a model tree ensemble  (Jung et al. , 2011)  to evaluate modeled forest biomass. For soil carbon stocks, the simulated soil carbon density was compared with the Harmonized World Soil Database (HWSD, 0-1 m depth, FAO/IIASA/ISRIC/ISSCAS/JRC, 2012) and the Northern Circumpolar Soil Carbon Database (NCSCD,  Hugelius et al. , 2013). Since the model results for soil carbon are not fully comparable to NCSCD due to lack of peatland carbon accumulation and cryoturbation processes in ORC-HL, metrics were not applied to soil carbon for establishing a model score. All gridded observation-derived data were aggregated at 2 • × 2 •. Apart from gridded data products based on satellite observations, independent forest inventory data at country/region level as compiled by  Pan et al.
Metrics for model evaluation Different metrics can be used to quantify the agreement between model results and observations, including Pearson correlation, model-to-data deviation, mean error, root mean square error (see  Kelly et al. , 2013; Cadule et al. , 2010). However, most of these metrics do not consider observational uncertainty. When there are multiple observations available and no particular dataset can be proved to be more accurate than others, which is the case for land cover, the choice of an observational dataset for model evaluation may have a large influence on the model performance score. In order to quantify the agreement between simulated and observed fields, as well as to integrate the uncertainty of observations, a metric normalized by observational uncertainty (Skill, S) was defined to evaluate model performances in terms of PFT fractional cover, GPP and forest biomass.
Metrics for PFT fractional abundance evaluation For PFT fractions, a beta diversity metric   (Poulter et al. , 2011; Ottlé et al. , 2013). For every grid cell c, beta diversity between model and observational dataset i (β c,M_Oi ) was calculated as: β c,M_Oi = 2 n k (V k,c,M -V k,c,Oi ) 2 (7) where V k,c,M is fractional abundance for PFT k and for grid cell c, simulated by model; V k,c,Oi is fractional abundance for PFT k and for grid cell c, from observational dataset i ; and n = 11, the number of natural PFTs. Similarly, the disagreement between two observations was quantified using β c,Oi _Oj , as: β c,Oi _Oj = 2 n k (V k,c,Oi -V k,c,Oj ) 2 (8) where V k,c,Oi and V k,c,Oj are fractional abundances from different observations i and j separately. β is bound to the interval [0, √ 2], with higher values representing larger discrepancies between two PFT maps.
To take into consideration uncertainties of the different satellite land-cover products (Sect. 3. 2), we use the mean β c,M_O of the model vs. all datasets normalized by the mean β c,O_O of all combinations between different datasets. In order to derive a bounded score, with higher values representing better model performance, the metric for the model skill at simulating vegetation distribution in every grid cell (S V,c ) was defined as: S V,c =   1 P i =j β c,Oi _Oj   1 Q i =1 β c,M_Oi ( 9 ) where P is the number of all combinations between different datasets; Q is the number of datasets. Figures.
Back Close Full The S V,c of each grid cell was averaged over the Northern Hemisphere (20-90 • N) to get an overall score (S V ). In the calculation of S V , grid cells where mean β c,O_O is higher than mean β c,M_O for both models (S V,c > 1) were excluded, because in these pixels the uncertainties in the observational data are too large to qualify them for model evaluation -the choice of dataset might significantly alter the model evaluation result. Grid cells where both model and datasets have 100 % bare ground (Sahara Desert and Greenland), and grid cells with a crop fraction higher than 0. 5, were masked out (18 % of the total number of land points in that part of the Northern Hemisphere included in the study). The same rules were also applied to the calculation of regional average β c,M_O and β c,O_O.
Back Close Full Figure  3  shows the spatial pattern of β between the three observational datasets (ESA, GLC and VCF), and mean D among them for different PFT groups. The β between different datasets show a higher agreement for ESA vs. GLC (an average β of 0. 25) and lower agreement for VCF vs. ESA or GLC (average β of 0. 37 and 0. 35 respectively). ESA and GLC legends are based on the FAO Land Cover Classification System (LCCS); while in VCF, the original 1 km continuous field data  (DeFries et al. , 2000) , in which the forest fractional area is given for each grid cell instead of a discrete classification scheme, was aggregated to 0. 5 • resolution for ISLSCP II under the guidance of IGBP (International Geosphere-Biosphere Programme), by  DeFries and Hansen (2009). LCCS uses a low threshold (15 %) of tree cover for forest definition, whereas IGBP uses a threshold of 60 %  (Poulter et al.
Metrics for GPP and forest biomass evaluation GPP and forest biomass were evaluated using gridded observational data containing uncertainty estimates. The metric for model performances was defined as: S G,c or S B,c = σ O X c,M -X c,O (11) where S G,c /S B,c is model skill at simulating GPP or forest biomass for grid cell c; X c,M is GPP or forest biomass for grid cell c, simulated by model; X c,O is GPP or forest biomass for grid cell c, from observation; and σO is the SD of the observation. Introduction.
The forest areas simulated by the dynamic simulations and estimated from the landcover products were aggregated to country level and compared with independent forest area from national forest inventories (Pan et al. , 2011) (Table  4 ). In OLD, forest areas are systematically overestimated, especially for Asian Russia and Canada. The bias is decreased in NEW, for which most of the differences are less than 30 % except for an overestimation in Canada (50 %). This overestimation is, however, within the differences between the three land-cover products and the forest inventory data at country scale (Table  4 ). Forest areas estimated by VCF are systematically lower than inventory data, due to the difference in forest definition mentioned previously. The largest underestimation of VCF occurs in Asian Russia, where the vast taiga-tundra transition zones with relatively sparse trees make the definition-related biases more prominent.
Distribution of specific groups of Plant Functional Types For the different PFT groups described in Sect. 3. 3. 1, the Northern Hemisphere average dissimilarity index (D) is plotted in Fig. 7  for OLD and NEW vs. observations, as well as between different observational datasets. For the broadleaf evergreen group, D is small for both OLD and NEW, and similar to the uncertainty in the data, because the broadleaf evergreen fraction is smaller than other tree PFT groups in temperate Figures.
Back Close Full and cold zones. For the broadleaf deciduous, needleleaf evergreen and needleleaf deciduous groups, the average D for NEW vs. the three datasets is reduced (i. e. , improved) by 53, 13 and 67 % respectively, compared with OLD. The OLD overestimation of broadleaf deciduous area in Canada, Scandinavia and European Russia is corrected in NEW (Fig. 8b ). The large underestimation of needleleaf evergreen in OLD is partly corrected in NEW, but a significant underestimation of the needleleaf evergreen coverage still exists in southern Siberia and western Canada (Fig. 8c ). For needleleaf deciduous, the unrealistically high fractions in subarctic regions in OLD are corrected in NEW, but needleleaf deciduous fractions in southern Siberia and Canada are still higher than observations, at the cost of needleleaf evergreen (Fig.
Case study for Siberia, using regional land-cover datasets For Siberia, the same metrics were calculated based on five observational datasets (ESA, GLC, VCF, OSIB and IIASA). As shown in Fig. 9a , the average β for NEW vs. all datasets is significantly reduced compared to OLD along all longitudes, with a larger reduction (improvement) in central Siberia and the most eastern part of Russia and smaller β for GLC and IIASA (0. 23), VCF and OSIB (0. 28), ESA and GLC (0. 29). OSIB and VCF both have lower fractions of tree PFTs than the other three maps. In particular, the needleleaf deciduous fractions in OSIB and VCF for the densest forest areas are less than 0. 65, while other maps can reach 0. 85. The model skill (S V ) that integrates observational uncertainty for Siberia is shown in Fig. 9b  (OLD) and c (NEW).
, 2013) , the model results are not fully comparable to NCSCD. The spatial patterns of soil carbon from OLD and NEW are similar (Fig. 11c ). Over the grid cells present in NCSCD, the total soil carbon simulated by OLD and NEW is 263 and 283 Pg C, respectively. A comparison of soil carbon simulations from several land surface models coupled with climate models in CMIP5  (Todd-Brown et al. , 2013)  suggested that most models cannot reproduce grid-scale variation in soil carbon; and that the substantial disagreement between the HWSD and NCSCD datasets and their lack of quantitative uncertainty estimates limit their ability for benchmarking land carbon models. Given the large carbon storage in northern high latitude soils, the ability to accurately simulate high latitude processes such as permafrost, wetland and peatland carbon accumulation, is a prerequisite for realistic projections of future climate-carbon feedbacks.
Conclusions This study has presented an improved parameterization and a calibration of Northern Hemisphere vegetation dynamics in the ORCHIDEE process-based ecosystem model, based on a version that includes frozen soil moisture and its impacts on plant productivity. Keeping the original model's concept of plant functional types, we modified the processes that influence tree existence, mortality and competition. A new performance metric applicable for DGVM evaluation in terms of vegetation fractional cover was used to evaluate ORCHIDEE, which integrates uncertainties in different land-cover maps. The new version of the ORCHIDEE vegetation dynamics module shows marked improvement in the simulated PFT distribution compared to the previous version. A more realistic simulation of the northern tree limit is obtained, as well as of the distribution of evergreen and deciduous conifers in the boreal zone.
• N. All runs were conducted at 2 • resolution. The climate forcing files were from the 6 hourly CRU-NCEP dataset (http://dods. extra. cea. fr/store/p529viov/cruncep/V4_1901_2012/readme. htm), resampled from their original 0. 5 • data. CRU-NCEP is widely used as standard climate forcing in current offline terrestrial models, such as MsTMIP (Multi-scale synthesis and Terrestrial Model Intercomparison Project, Huntzinger et al. , 2013) and TRENDY (trends in net land-atmosphere carbon exchange over the period 1980-2010). Tests with different resolutions were carried out, showing quite similar results in the simulated vegetation distribution and carbon fluxes and pools (results not shown), indicating that the results presented below do not depend significantly on the spatial resolution of input climate and soil property data within the tested resolution range [0.
The average values of β over Siberia for NEW vs. ESA, GLC, VCF, OSIB and IIASA are 0. 59, 0. 46, 0. 38, 0. 35 and 0. 41 respectively, equivalent to 0, 10, 51, 45 and 26 % reduction compared with OLD, respectively. The average β between different datasets is 0. 37, with larger β between ESA and VCF (0. 50) and between GLC and VCF (0. 47), Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |.
Are they coeval terranes metamorphosed in different tectonic settings before being assembled, or are greenstone belts discordant supracrustal rocks deposited on an older gneissic basement? Answering this question has strong implications on the geodynamic processes controlling the maturation and stabilisation of cratons after the Archean-Proterozoic transition. In order to do so, we present combined field, petrological and geochronological datasets of spatially associated low-, medium-and high-grade metamorphic rocks of north-western Ghana, in the south-eastern West African Craton. The datasets are used to explore the mechanisms driving the exhumation and the tectonic assembly of the distinct metamorphic domains during the Eburnean orogeny.
, 1996 , Gasquet et al. , 2003)  argue for a single orogenic cycle. In this view, volcano-sedimentary basins are considered to be contemporaneous equivalents of volcanic belts and to illustrate lateral facies variations  (Leube et al. , 1990; Eisenlohr & Hirdes, 1992; Hirdes et al. , 1996). The monocyclic model implies that terranes of contrasting metamorphic grade are coeval, and that low-grade metamorphic units represent supracrustal equivalents of high-grade migmatitic gneisses. Consequently, variations in the metamorphic conditions of coeval terranes would result from differential exhumation. A limited number of geochronological data across the West African Craton point to a prolonged period of metamorphic overprint, which lasted c. 70 Ma. It spans from 2153±13 Ma given by a Sm-Nd garnet-whole rock isochron age  (Boher et al.