dataesr/scientific-paragraphs-categorization huggingface dataset:
All fire scars recorded on the cross section occurred in the early growing season, indicating late spring or early summer fire timing. These data also strongly suggest that fire severity was low because the same trees survived at least eight fires, suggesting relatively low-temperature surface fires characteristic of open, pre-Euro-American pine forests. The stand-scale fire frequency identified at CCESR was comparable to the results of other crossdated fire histories in red pine stands conducted in the region  [29, 51, 52] , though it should be noted that with fire dates derived from only two trees this is the minimum estimate of fire activity and other fires may have burned through the stand but gone unrecorded in the tree-ring record. CCESR shared a number of fire dates in common with the nearest published records, including 1909, 1879, and 1849 CE, though none of these years are shared by more than two sites and few would be considered widespread fire years.
Conclusion The charcoal and tree-ring records we report here provide an opportunity to examine the different records produced as a result of the spatial and temporal scale of fire signals recorded by these proxies. The charcoal record is clearly recording landscape-scale production of charcoal from fires of all types, while the tree-ring data document highly localized, low intensity surface fires in more forested areas. This emphasizes the complementary nature of these two proxies for both better understanding past patterns of fire activity, and the uncertainty therein. There are several consistencies between the records, however, particularly the decrease of the area burned (decrease of the charcoal abundance per time unit  [42] ) and the decrease of the fire activity recorded from the scars in the 21 th century.
Acknowledgments: We thank  Cedar Creek Ecosystem Science Reserve  for permission to conduct field work. Dan Engstrom directed the collection of the short sediment core and provided the 210Pb analysis. Members of the Novus III workshop assisted with sample collection. Jakob Hanschu  provided charcoal data. Margaret Bialecki  assisted in preparation, dating, and analysis of wood samples. We are grateful to the editor, and several anonymous reviewers whose suggestions led to an improved manuscript.
Methods Table  1  provides a subset of the data used in the geochemical plots, and the text below provides a brief summary of analytical Fig. 2. Isotope stratigraphies for the FAB sites (Holes 1440B and U1441A) and boninite sites (Holes U1439C and U1442A). Both of the FAB and boninite stratigraphies may be subdivided into axial (lower) and off-axis (upper) groups. The axial-FAB subdivide on trace element criteria into D(depleted)-FAB, the dominant N(normal)-FAB and E(enriched)-FAB: these differ in εNd i values, which increase from D-through N-FAB to E-FAB. Off-axis FAB have distinctly lower εNd i than axial-FAB, and off-axis boninites mostly have lower εNd i and εHf i than axial-boninites as well as higher 206 Pb/ 204 Pb isotope ratios. All FAB have higher εHf i and lower 206 Pb/ 204 Pb than all boninites.
Analyses of USGS reference materials, BCR-2, BIR1a and BHVO-2, run as unknowns demonstrate excellent agreement both between the two laboratories and with accepted values (Appendix B, Table  B. 3). We determined Sr isotope ratios at both GIGCAS and the MAGIC using a Thermo Finnigan Triton thermal ionization mass spectrometer (TIMS) (Appendix B, Sections 3 and 4). Both laboratories report the Sr isotopic ratios relative to 87 Sr/ 86 Sr of NBS-987 = 0. 710248. Appendix B, Table  B. 2 gives international standard data obtained by the two laboratories. We measured Pb isotope ratios on a Thermo Neptune MC-ICP-MS at the University of Southampton UK, using a double spike to correct for instrumental mass fractionation (Appendix B, Section 5). Procedural blanks range between 30-95 pg Pb. NBS SRM 981 values achieved during the measurement period were 206  Several diagrams in this paper combine our data with published data from other laboratories.
Principal Hf, Nd, Sr and Pb element characteristics = 3. 8 ± 0. 2[1σ ] and D-MORB ((Ce/Yb = 3. 0 ± 0. 2[1σ ]) (data of For the boninite sites (Fig. 3b-c ), all samples are significantly enriched in Sr and Pb relative to the surrounding REE, a characteristic of all arc-related lavas. Unlike most arc lavas, however, they exhibit pronounced positive Hf anomalies, features commonly associated with subduction initiation (e. g. Hickey-Vargas, 1989; Pearce et al. , 1999). Their low concentrations of the subductionimmobile HREE indicate very depleted mantle sources, significantly more depleted than FAB sources. Their LREE enrichments Ce/Yb = 3. 05 ± 0. 44[1σ ]) are, however, greater than most FAB, so requiring modification of this depleted mantle by a LREE-enriched subduction component. Thus Nd must have been subductionmobile during boninite genesis.
The mean N-FAB composition and the off-axis FAB plot close to the Palaeozoic errorchrons plotted by  Yogodzinski et al. (2018)  for IODP Site U1438: the combined basalt dataset from Sites U1438 and U1439-1442 gives similar 450 Ma errorchrons on both diagrams (Fig. 5c-d ), though with considerable scatter. Overall, therefore, our isotope data support the concept of  Yogodzinski et al. (2018)  and  Shervais et al. (2019)  that subduction initiated within a mantle domain that had already experienced one or more ancient depletion events, though more work is needed to ascertain precise ages and details. In any event, this mantle domain provides the mantle wedge end-member needed to explain the compositions of the subsequent subduction events.
Characterizing the subduction component using isotope-element ratio covariations Plots of the form εHf i -x/Hf are particularly effective at interpreting Hf anomalies on the extended REE diagram (Fig. 3b-c ) in terms of subduction components, as any mixing lines are then linear and so easier to interpret (e. g. Barry et al. , 2006). In Fig. 6a , x = Sm, chosen because Sm is closest to Hf in its bulk partition coefficient during mantle melting and fractional crystallization. The axial-FAB data plot at the upper (MORB) end of the ambient Philippine Sea array, while the off-axis FAB lie on an extension of this array. As can be seen in all four diagrams (Fig. 6a-d ), an average axial-FAB composition is the optimum choice for the isotopic mantle end-member for the subsequent generation of boninites.
Being a boninite source, this end-member is depleted in incompatible elements compared to the FAB mantle Fig. 6. Use of εHf-x/Hf and εNd-y/Nd plots (where x is Sm, Ti or Nd and y is Hf) to constrain the εHf and εNd values of mixing end-members. The diagrams help define two trends: the first from M (residual FAB mantle) and a shallow (amphibolite-facies) slab melt (at S 1 ); and the second from a point on that trend representing a hybrid mantle wedge (H) to a component containing pelagic sediment and an altered oceanic crust (AOC) component (at S 2 ). Data sources for the ambient (Philippine Sea) mantle array are given in the caption to Fig. 5. The gradient of the ambient mantle array reflects the difference in incompatibility between x and Hf or Nd (positive if x is less compatible). source (Fig.
Similarly, increasing temperature decreases Sm/Hf and the estimated εHf. An error bar of ±0. 5 epsilon units covers the range of 800-1000  Basement composites (subscript C) represent whole-core sections (lavas plus interstitial materials), analyzed by  Chauvel et al. (2009). Site 801 MORB and TAB (Top Alkali Basalt) data were from pure basalt cores using εHf and εNd data for the data points  (Pearce et al. , 1999)  with additional data on εNd only  (Hauff et al. , 2003)  used to establish their dispersion. Fig. 7b -c include further sediment data from  Plank et al. (2007)  and  Vervoort et al. (2011). Mixing lines between M (residual FAB mantle) and S 1 (subduction component 1) and between H (hybrid mantle wedge) and S 2 (subduction component 2) constrain the compositions of bulk subducted materials.
Conclusions 1. The first stage of magmatism following subduction initiation is associated with sea-floor spreading following subduction initiation and results in the eruption of forearc basalts (FAB) at IODP Sites U1440 and U1441. Isotope data confirm that these FAB lack a subduction component and so can be used to characterize the ambient mantle reservoir prior to the input of the subduction component. Notably, this reservoir had 'Indian' provenance and had experienced an ancient (possibly Palaeozoic), garnet facies depletion event, similar to that proposed by  Yogodzinski et al. (2018)  based on the slightly younger, and more trench-distal, basalts drilled at IODP Site U1438. 2. The first evidence of subduction is recorded by the axial boninites at IODP Sites U1439 and U1442. Their isotopic compositions follow mixing trends between residual mantle of 'Indian' provenance and oceanic crust of 'Pacific' provenance.
Acknowledgements We thank the  International Ocean Discovery Program (IODP)  for samples and infrastructure. We appreciate the contributions of the Staff Scientist ( K. Petronotis ) and remaining members of the IODP Exp. 352 Scientific Party as well as the officers and crew, and scientific support staff, on the JOIDES Resolution. We thank  Gene Yogodzinski  and an anonymous referee for their helpful recommendations and  Tamsin Mather  for her editorial support. Hong-Yan Li  additionally thanks the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  (Grant No. XDB18000000 ) and the  Chinese National Program on Global Change and Air-Sea Interaction  ( GASI-GEOGE-02 ). Shervais, Ryan and Reagan thank the  USSSP for initial Expedition  funding and the  NSF  for grants  OCE1558689 ,  OCR1558855  and  OCE1558647  respectively.
S-wave velocity model misfit In order to construct an isotropic S-wave velocity profile at each node a two-step inversion process was chosen, as described in Sect. 2. 4. Examples of the results of the neighbourhood algorithm from three locations in the Sakarya Terrane, Armutlu Block and Istanbul zone are shown in Fig. 5. The best 1000 models from the neighbourhood algorithm occupy a much smaller range for the Sakarya Terrane and Armutlu Block examples. The broader range for the Istanbul zone example shows that the data here provide weaker or possibly conflicting constraints on the model velocity profile. To improve the data misfit in such cases, a linearized inversion approach with surf96 (Herrmann, 2013) is used to find an optimum model. Supplement Fig. S7  shows the final fit of the dispersion curves calculated at each of the nodes shown in Fig.
Acknowledgements. George Taylor is supported by the  Leeds-York Doctoral Training Partnership  of the  Natural Environment Research Council (NERC) ,  UK. Gregor Hillers acknowledges support through a  Heisenberg fellowship  from the  German Research Foundation  ( HI 1714/1-2 ). The DANA array was part of the  Faultlab  project, a collaborative effort by the  University of Leeds, Kandilli Observatory and Earthquake Research Institute , and  Sakarya University. Major funding was provided by the  UK NERC  under grant  NE/I028017/1. Equipment was provided and supported by the  NERC Geophysical Equipment Facility  ( SEIS-UK ) loan 947. We would like to thank  Sven Schippkus  and an anonymous reviewer for their detailed reviews that helped improve the paper.
NAHR MENASHE DEPOSIT Interpretation of 2-D and 3-D seismic data (see Methods in the GSA Data Repository 1 ) from offshore Cyprus, Syria, Lebanon, and Israel has led to the recognition of a formerly unidentified basin-scale accumulation. This deposit, herein termed Nahr Menashe (Fig. 1 ), has an areal extent approximately equal to that of the Messinian Nile River (Eonile) fluvial valley fill (Abu Madi Formation) and a volume of >4150 km 3 (calculated from 2-D seismic data in two-way traveltime [TWTT] and using an interval velocity of 2925 m/s). From its position and morphology, as well as interpreted age and depositional environment, we show that the Nahr Menashe is one of the largest riverine accumulations associated with the terminal MSC, and that it deposited in a subaerially exposed, actively deforming Levant Basin.
ACKNOWLEDGMENTS Madof thanks  Chevron  for allowing publication, and  S. E. Baumgardner ,  E. G. Fathy ,  A. D. Harris ,  F. L. Laugier ,  M. J. Madof , and  K. Nakamura  for suggestions. Bertoni thanks the  John Fell Fund  ( Oxford University ) for support. We thank  D. Cosentino ,  Z. Gvirtzman , and an anonymous reviewer for thoughtful remarks, and  PGS, Spectrum , and  TGS  for use of seismic data. This is a  European Cooperation in Science and Technology (COST)  MEDSALT contribution.
Study population The GAZEL cohort was launched in 1989 among workers of the French utility company for energy production and distribution Electricité de France-Gaz de France (EDF-GDF)  (Goldberg et al. , 2015). At inception, the cohort included 20,625 participants, men aged 40-50 years and women 35-50 years, distributed all over France. At inclusion, each participant completed a self-administered comprehensive questionnaire and then was followed up annually completing a new self-administered questionnaire. In addition, residential history has been collected and geocoded for each participant since 1989. In the present study we set the baseline in January 1996, reflecting the first date when biomonitoring-based air pollution data were available. Thus, we considered all the participants with any available lifetime residential addresses since 1996.
Since moss biomonitoring data were collected throughout France, mostly in rural areas, the validity of our nation-wide modelling would be greatest in non-urban areas. Consequently, for each participant, we classified her/his residential area's population density into low, intermediate, or high according to Insee (the French national office of statistics) as described in Supplementary Appendix 1. We restricted our study population to participants living only in low to intermediate population density areas (referred to as 'rural' hereafter), during the study period. Any participant who resided in a high population density ('urban') area at least once during follow up was excluded. Further, to limit exposure misclassification, the annual residential history of "rural" participants was used to exclude those living longer than 20% of their follow-up outside of mainland France.
Participants who died before 1996 were excluded. The final dataset included 11,382 participants. Vital status data were collected from the EDF-GDF company until March 2017. Because of the low-turnover rate and excellent retirement benefits in this company, the company records represent a reliable source of information on vital status of current and past employees  (Goldberg et al. , 2001). Causes of death were recorded by the French national cause-of-death registry and coded using the international classification of diseases (ICD: ninth revision ICD-9 until 1998, and tenth revision ICD-10 thereafter). Natural-cause mortality was analysed after discarding participants who died from accidental or violent deaths.
Exposure to atmospheric metals In France, moss biomonitoring of atmospheric metals is conducted by the French National Museum of Natural History, which manages the BRAMM program. The BRAMM database includes four surveys  (1996, 2000, 2006, and 2011)  during which 13 metals were measured: Aluminium (Al), Arsenic (As), Calcium (Ca), Cadmium (Cd), Chromium (Cr), Copper(Cu), Iron (Fe), Mercury (Hg), Sodium (Na), Nickel (Ni), lead (Pb), Vanadium (V), and Zinc (Zn). The objective of this programme is to monitor background atmospheric metal levels, mainly in forest sites and away from major industrial, urban, and traffic sources. Details on the sampling protocol in the field, preparation in laboratory, and analytical procedure of the BRAMM program are available elsewhere  (Lequy et al. , 2016). Briefly, at each survey, mosses were collected at approximately 500 sampling sites in mainland France (Supplementary Fig.
1 ). Within one week, moss samples were shipped to the laboratory where they were cleaned before drying and grinding. Finally, moss samples were analysed for their complete metals content after dissolution in nitric acid. Sampling period and analytical techniques varied between surveys (Supplementary Appendix 3). Analytical uncertainty was assessed with respect to moss standards, which enabled us to apply a correction factor to all our data  (Lequy et al. , 2016). Measurements are given in μg•g -1 of dry weight: they represent the content of each moss sample at the moment of sampling. Although duration of retention of metals in moss has not been definitively established, it is generally believed that mosses integrate metals over approximately up to three years  (Markert et al. , 2003). This study followed the guidelines from the ICP-Vegetation (https://icpvegetation.
75-0. 92, attesting a model performance similar to that of PM 10 or PM 2. 5 in France or Europe  (Bentayeb et al. , 2014; de Hoogh et al. , 2018; Vienneau et al. , 2013). The kriging interpolation was performed using R software version 3. 3. 2 (R Core Team, 2015), and the package GSTAT  (Pebesma, 2004). To each participant and each residential address at the year of an available moss-biomonitoring survey  (1996, 2001, 2006, and 2011) , we assigned atmospheric metals exposure based on the krigged map of the corresponding year (see details in the statistical analyses section). We imputed the exposure to arsenic in 2000. Details on the management of the residential addresses are available in Supplementary Appendix 4.
Data-driven subsets We used Principal Component Analysis (PCA) to define a small number of composite variables that capture the essential underlying groupings of highly correlated variables among the individual atmospheric metals as measured in moss and assessed at each person's address  (Jackson, 2003). Due to eliminating Hg from this analysis because of missing values in the first BRAMM survey, the PCA was conducted on 12 atmospheric metals after log-transforming time-varying single metals. The PCA was performed using R software and the package ade4  (Chessel et al. , 2004; Dray et al. , 2007). The PCA on the overall exposure yielded three principal components (Fig. 2 ): 1. mainly Fe, Cr, V, and Al (50% of variance explained; all are abundant metal of earth's crustwe called this the crustal metalcomponent), 2.
Covariates Covariates used in these analyses were collected from the baseline and the follow-up questionnaires. The following data were used as individual potential confounders in the main or sensitivity analyses (see the statistical analyses section below). For non-time-varying variables, we included the information collected at baseline  (1996)  or, if unavailable, at the nearest available year: sex, occupation class (blue collar-workers, administrative clerks, foremen/supervisors, and executives), alcohol intake (alcohol consumption was assessed in 1989 and subjects classified according to the weekly intake of alcohol beverages as abstinent, light drinker, moderate drinker, heavy drinker), marital status (single, married or equivalent, separated, divorced, or widowed), and level of education (Low: primary and middle school diploma, Intermediate: professional certification, and High: high school diploma, university, or advanced technical certification).
(2013). We further extrapolated these concentrations over the 1990-2015 period using the method developed in the ELAPSE project  (de Hoogh et al. , 2018). We applied back-extrapolation at regional level (i. e. European Classification of Territorial Units for Statistics; NUTS1, n = 8) using dispersion model estimates to derive exposures for other years. As such, we estimated PM 2. 5 exposures for all addresses and all participants in  2010, and extrapolated to 1996, 2000, 2006, and 2011. Distance to major roads (data from the National Institute of Geographic and Forest Information) for each individual was computed as the average distance from the home address for each year of the BRAMM surveys. We also attributed a population density indicator (low or intermediate) and an indicator for NUTS1 region to account for potential regional differences in France.
We tested potential effect modification by running the adjusted Cox model on population subsets for natural-cause mortality. Analyses of risks due to metal exposure were stratified on the following covariates: sex, as females have been reportedeven if not consistentlymore susceptible to air pollution effects  (Clougherty, 2010) ; smoking status (never-smoker with zero pack-years, versus ever-smoker); population density (low versus intermediate); and distance to major roads (< 1000 m and > 1000 m). We then formally tested for interactions. All statistical analyses were performed using R software version 3. 3. 2 and the package SURVIVAL  (Therneau, 2015; Therneau and Grambsch, 2000).
Indeed, the current air pollution levels do not allow establishing a threshold below which no harm is observed. A major strength of this study is that we used a large populationbased cohort: we included a detailed set of individual-level covariates, and we assessed exposure to atmospheric metals at the home address and also accounted for address history. Our biomonitoring data allowed for interpolation by kriging over a 2-km grid, providing a unique dataset of exposure to atmospheric metals for a 15-year long follow-up. Complementing previous studies at ecological level  (Sarmento et al. , 2008; Wolterbeek and Verburg, 2004) , our individual-level study supports the idea that mosses are a credible tool to assess exposure of the general population to air pollution. Our results are based on a very innovative method exploiting mossbiomonitoring, making it possible to distinguish between crustal, anthropogenic, or marine sources of pollution.
2a , bottom panel). A posteriori flux estimates inferred from GOSAT lie between a priori values and the fluxes inferred from OCO-2, reflecting the superior data density of OCO-2; fluxes inferred from GOSAT are insignificantly different from a priori values during early 2016 due to a very low density of measurements during this period. Differences in the spatial and temporal CO 2 flux distributions (Fig. 3a ) demonstrate current limitations in our ability to infer spatial distributions of CO 2 fluxes  26, 27. We find that the a posteriori distributions of carbon flux over the El Niño period resemble the E-W dipole pattern of water storage (Fig. 4a ), with larger positive (negative) anomalies towards the east (west) corresponding to larger positive (negative) CO 2 fluxes. The El Niño period also saw anomalous fire activity in the 2015 dry season (Fig.
Although caution should be exercised when interpreting regions smaller than 1000 km, these emission focal points are a robust feature of our analysis that extends back through the GOSAT record to 2009. We do not rule out a role for regional systematic retrieval errors  29  , but comparison to sparse independent data (Supplementary Fig. 12 ; Supplementary note 2) and the results from extensive sensitivity experiments (Supplementary Figs. 13 14 15 16 ; Supplementary note 3) support our results. The magnitude and approximate timing of the inferred seasonal cycle of net fluxes is consistent with the ORCHIDEE land surface model (Fig. 2b ; Supplementary Discussion), although the model has larger uptake later in the year.