dataesr/scientific-paragraphs-categorization huggingface dataset:
Montealegre et al. (2014)  calibrated a logistic regression model to relate post-fire LiDAR metrics to field CBI measures over Mediterranean forests in Spain. The output probabilities of the model were further grouped into different burn severity levels. Several studies have used a combined approach of airborne LIDAR and passive sensors to estimate post-fire effects. Kwak et al. (2010) , for instance, used LiDAR data to estimate the degree of physical damage (loss of canopy cover), while NDVI values from a multispectral sensor were used to determine the biological damage (vegetation vitality). Physical and biological damage were subsequently combined to classify the affected area into four levels of fire damage. Structural changes in the forest cover related to fire effects were retrieved by McCarley (2017a) from a bitemporal LiDAR dataset along with several spectral indices derived from Landsat data in a temperate coniferous forest in the U.
S. The best relationships between multispectral and LiDAR data occurred for changes in canopy cover whereas the relationships with LiDAR metrics representing changes in mid and lower strata weakened and became poorly correlated with those LiDAR metrics representing changes near the surface layer. Wulder et al. (2009)  integrated two transects (pre-and post-fire) of data collected with an airborne profiling LiDAR system with Landsat imagery to relate changes in forest structure to post-fire conditions estimated by spectral indices over a burned boreal forest in Canada. Due to the lack of spatial coincidence between the two LiDAR transects, the post-fire image was segmented using an object-based approach and structural metrics were summarized for each segment, representing homogeneous vegetation patches, as well as for the total length of the transects.
Ballhorn et al. (2009)  compared the height difference between burned and adjacent unburned areas along 79 airborne LiDAR transects over peat swamp forests in Central Kalimantan, Borneo, during the 2006 El Niño episode. Soil consumption estimates were in close agreement with field measurements. Reddy et al. (2015)  estimated soil consumption by a peat fire in North Carolina and Virginia, USA, based on the elevation change from bitemporal airborne LiDAR datasets and compared the results to consumption values estimated using the First-Order Fire Effects Model (FOFEM). They found LiDAR estimates more accurate than modeled estimates due to the limited representation of peat depth in the LANDFIRE fuel model input layer. Additionally, they analyzed the influence of LiDAR elevation errors in the carbon loss estimates using a Monte Carlo simulation to find out that LiDAR elevation errors did not significantly contribute to the uncertainty in the soil carbon loss.
Integrated analysis of LIDAR and passive optical sensors have also been performed for BA mapping. Garcia et al. (2017) , for instance, integrated post-fire LiDAR data and bitemporal Landsat data. Pre-fire biomass was estimated using a two-step approach. First, a LiDAR model was calibrated using field data to estimate biomass over the study area. Second, in order to derive pre-fire biomass values, LiDAR-based estimates across the 2 km buffer were extrapolated over the whole area using pre-fire Landsat data. By comparing pre-fire to post-fire biomass values, it was possible to compute the biomass consumed by the fire.
Global products After the first attempts to generate global BA products in the late 1990s, the early 2000s provided the mature conditions to release the first global semi-operational BA datasets. Building on the experience of NOAA-AVHRR BA algorithms  (Barbosa et al. , 1999a; Fernández et al. , 1997; Kasischke and French, 1995; Langaas, 1992; Martín and Chuvieco, 1995; Pereira, 1999) , the new BA products were mainly based on the MODIS sensors, on board NASA's Terra and Aqua satellites, and the SPOT Vegetation (VGT) sensor (Table  2 ). The first global BA product at coarse resolution was produced by the Joint Research Centre of the European Union. It was named Global Burned Area (GBA2000) and was based on daily VGT images acquired throughout the 2000. This product had 1 km 2 spatial resolution and provided monthly estimates of BA.
The BA detections were based on seven regional algorithms adapted to different fire conditions  (Tansey et al. , 2004b). In parallel to the GBA project, the European Space Agency developed the GLOBSCAR BA product for the same year 2000. This global monthly product was derived from daytime ERS-2 ATSR-2 data with a nominal pixel size of 1 km 2. BA detection relied on the combination of a contextual and a fixed threshold algorithm  (Simon et al. , 2004). Following the experience of GBA, other global BA products have been released by European institutions: the L3JRC  (Tansey et al. , 2008)  covering the period from 2000 to 2007; the Globcarbon  (Plummer et al. , 2006) , from 1998 to 2007, and the Copernicus GIO_GL1_BA products, all at 1 km spatial resolution. All these products were derived from VGT images (in Globcarbon, ATSR images were used as well).
The exception is the most recent version of the Copernicus GIO_GL1_BA, which after 2013 has 333 m resolution and is derived from PROBA-V data (https://land. copernicus. eu/global/products/ba, last accessed July 2018). In a different context, the Fire_CCI project (part of the European Space Agency's Climate Change Initiative) has generated three global BA products over the last few years. The first one was named FireCCI41 and it was based on 300-m resolution MERIS images from the ENVISAT satellite, covering the period from 2005 to 2011. The BA algorithm was a hybrid and two-phase approach, where MERIS reflectances were supplemented with first MODIS HS to detect the most clearly burned pixels and then contextual criteria were applied for improved delineation of burned patches (Alonso-Canas and  Chuvieco, 2015; Chuvieco et al.
Name of burned area dataset The MCD64A1 product in combination with additional variables on fuel properties and emission coefficients has in turn been used to produce the Global Fire Emissions Database (GFED). Current versions of the GFED (designated GFED4 and GFED4s) include data from the MCD64A1 collection 5 product, as well as the ATSR sensor for the pre-MODIS era  (1995) (1996) (1997) (1998) (1999) (2000). GFED4s adds an estimation of the area burned by small fires (< 100 ha), which are commonly not detected by global products based on coarse resolution sensors. GFED4s estimates the contribution of those small fires by BA to MODIS active fire hotspots located outside of burned patches mapped in the MCD64A1 BA product. The BA allocated to each "outside-of-burn" hotspot is in turn estimated using postulated relationships between dNBR, the number of "withinburn" hotspots, and the BA actually mapped in the MCD64A1 product  (Randerson et al.
Regional products Several countries have been operationally developing BA mapping products in the framework of various fire monitoring systems. In the United States, the Monitoring Trends in Burn Severity (MTBS) project was sponsored by the Wildland Fire Leadership Council (WFLC) and implemented jointly with the U. S. Geological Survey (USGS) and Forest Service  (Eidenshink et al. , 2007). The project's objective was the systematic production of BA maps and associated burn severity information. Among the different data employed by the MTBS project, pre-fire and post-fire Landsat TM, Enhanced TM Plus (ETM+), and OLI imagery are mainly used for the computation of the dNBR and the subsequent generation of estimated burn severities. The U. S. Geological Survey has also recently developed the Landsat Burned Area Essential Climate Variable (BAECV) project, which covers the conterminous U.
S. (Hawbaker et al. , 2017). The BAECV algorithm was used to identify burned areas ≥4 ha in every Landsat TM and ETM + images with < 80% cloud cover from 1984 through 2015 across the conterminous territory of USA. A modified version of the BAECV algorithm has been developed for use with Landsat OLI data. New Landsat BA products using the modified BAECV algorithm are available through the USGS EarthExplorer (earthexplorer. usgs. gov) interface for Landsat TM, ETM+ and OLI data from 1984-present. The lack of harmonized BA information and a holistic approach for forest fire prevention in Europe motivated the European Commission services and the relevant fire services of each country to develop the European Forest Fire Information System (EFFIS)  (San-Miguel-Ayanz et al. , 2012). EFFIS is a comprehensive forest fire management system with its core applications based on remote sensing and geographic information systems (GIS), which currently supports the monitoring of fires in 41 countries in Europe, Middle East and North Africa (http:// effis.
jrc. ec. europa. eu, last accessed September 2018). EFFIS incorporates different modules, namely FIRE Danger Forecast, Active Fire Detection, Rapid Damage Assessment and post-fire modules. MODIS data are employed for the detection of hot spots and BA mapping (for fires over 40 ha) on a European scale. Subsequently, this information is integrated into a national GIS for further analysis at a country level. BA products of high resolution are also provided, upon demand, by the Copernicus Emergency Management Service (EMS). This service is based on the rapid acquisition, processing and analysis of satellite imagery and other geospatial datasets after a fire event. In Mexico and Central America, a semi-operational program to provide information on BA and active fire was established in early 2000s.
The system is operated by the National Commission for the Knowledge and Use of the Biodiversity (CONABIO;  Ressl et al. , 2009). Hot spots are mapped with AVHRR and MODIS data, following methods of  Flasse and Ceccato (1996)  and  Giglio et al. (2003). BA mapping is derived from NDVI and NBR values computed from MODIS-Aqua data. The Forest Research Centre in the School of Agriculture at the Technical University of Lisbon in Portugal has developed a national operational BA mapping system based on remotely sensed data. In particular, the system employs time series of Landsat MSS, TM and ETM + data for BA delineation for the time period from 1975 to present. The resulting maps are utilized for structural fire risk mapping and the products are operationally used by the National Forest Authority, the National Authority for Civil Protection and by large private landowners  (Nunes et al.
The Queensland government produces an annual BA map based on the  Goodwin and Collett (2014)  algorithm. The North Australia and Rangelands Fire Information (NAFI) system is a MODIS-based fire patch mapping system hosted by Charles Darwin University. The NAFI methodology involves differencing pre-and post-fire MODIS imagery with a subsequent segmentation and classification step and some user input (www. firenorth. org. au). For the other parts of Australia, BA mapping has been done ad hoc for some events, using supervised high-resolution satellite imagery, airborne burn mapping, ground-based surveys, or a combination of these methods. However, an automated algorithm for nationwide BA and severity mapping using Geoscience Australia's (GA's) Landsat and Sentinel-2 data cube infrastructure was recently developed and is close to operational.
Coarse resolution BA products have been often validated by comparing them with medium resolution data (Landsat-TM, SPOT-HRV or Sentinel-2 imagery). The Committee on Earth Observing Satellites (CEOS) Land Product Validation team  (Boschetti et al. , 2009)  recommends that reference fire perimeters be derived from a multitemporal pair of images to properly date the validation period. The reference sites should be properly documented (with standard metadata), discriminating between burned, unburned and unobserved data (either from clouds or technical issues). The sampling design is critical to make the most out of the reference data. Probability sampling designs ensure that accuracy inferences are possible at global scale. The earliest BA product releases were published along with validation analyses based on selective sampling of a few test sites  (Tansey et al.
, 2004a (Tansey et al. , , 2004b;; Roy et al. , 2005). Therefore, global accuracy inferences were not available (or they had no statistical meaning). Globcarbon  (Plummer et al. , 2006)  and L3JRC  (Tansey et al. , 2008)  were validated with independent data derived from 72 Landsat scenes globally distributed mostly from the year 2000. Roy and Boschetti (2009)  reported validation results for the MCD45A1 product using a set of 11 Landsat scenes distributed across southern Africa. Chuvieco et al. (   2008 ) validated a regional product for Latin America using 19 Landsat scenes and 9 China-Brazil Earth Resources Satellite (CBERS) scenes. The MCD64A1 collection 5 data were not formally validated, but some quantification of uncertainty was provided  (Giglio et al. , 2009; Giglio et al. , 2010).
, 2017). The first global product accuracy inferences  (Padilla et al. , 2014; Padilla et al. , 2015)  were probably lower than expected and even to some extent controversial among the BA algorithm development community. The commission and omission error rates of the most accurate product were 42% and 68%, respectively  (Padilla et al. , 2015). Recent improvements in input data (higher spatial resolution) are leading to an important increase in accuracy  (Roteta et al. , 2019). Ideally, any improvement of BA products should be tracked by validation analyses, which should be externalized from algorithm developers and performed by joint initiatives including competing agencies, thus ensuring the independence that any validation should have. If validation protocols require using higher spatial resolution images, validation of BA products generated from Landsat and similar resolution imagery can be challenging.
Long-term regional BA products are available at much finer spatial resolution, such as the 30-m MTBS dataset, which spans the United States, though these come at a cost of reduced spatial coverage. Improvements in cloud processing and distributed archive facilities (e. g. Google Earth Engine) may greatly help to carry out global analysis at medium spatial resolution. Temporal coverage of these sensors may also create difficulties to detect fires in areas with frequent cloud cover and rapid vegetation regrowth  (Hawbaker et al. , 2017; Padilla et al. , 2015). The combined use of optical and SAR data may help the BA detection in regions where optical sensors perform poorly on their own. The launch of ESA's Sentinel-1 satellite constellation overcomes some of the past limitations of SAR data for BA mapping, particularly in terms of temporal coverage.
Acknowledgements The authors wish to acknowledge  Maureen McFadden  for help with cell culture,  Rémi Delaunay  for his dedicated research in the follow-up of this project, and  Jérémy Ferrier  and  Renata Santos  for critical reading of the manuscript. M. Tasso acknowledges  CONICET  for funding destined to research and international mobility. This work was supported by the  NanoCTC  ( ANR-10-Nano-05 ) grant managed by the  French Agence Nationale de la Recherche  and by the  PSL   Nanopaint grant  ( Aux Frontières des Labex ) from  PSL Research University. Dr. Z. Lenkei  and  Dr. D. Zala  contributed equally to this work.
| INTRODUC TI ON The expansion of pasture and the effects of animal husbandry on Earth have received increased recognition over recent decades  (Phelps & Kaplan, 2017 : e. g. Asner, Elmore, Olander, Martin, & Harris, 2004 , Foley et al. , 2005 , Vitousek, Mooney, Lubchenco, & Melillo, 1997). However, little attention has been given to the representation of prehistoric animal production in land use and land cover reconstructions, despite archaeological evidence that pastoralism was important and expansive throughout the Holocene (Figure  1 , Supporting Information Figure  S1 ). Relatively few anthropogenic land cover change datasets account for pasture or rangeland (e. g. Erb et al. , 2007; Klein Goldewijk, Beusen, & Janssen, 2010) , and those that do are often based on extrapolations of modern conditions or problematic assumptions about human land use  (Phelps & Kaplan, 2017).
We used the group of cattle, sheep and goats as a control, to test whether the introduction of new domestic animal species had a significant impact on climatic niche development, as these three animals were present throughout the entire study period. We excluded remains that could not be identified as wild or domestic, for example, where the species of Bos and Equus and the genera of Caprinae and Suidae were not indicated. Assigning a date range to each archaeological assemblage required the incorporation of substantial chronological uncertainty. We first constructed a date probability distribution for each archaeological assemblage: where radiocarbon dates were available we generated a summed probability distribution (SPD:  Rick, 1987 , Shennan et al. , 2013). Calibrations were performed with the 'Bchron' package in R using either intcal13 or shcal13 depending on the hemispheric location of each site  (Haslett & Parnell, 2008; R Core Team, 2018).
We then performed the same permutation test on the wild species group. We performed niche dynamic analyses with the 'ecospat' package in R  (Di Cola et al. , 2017; R Core Team, 2018) , and plotted metrics with Python (Python Software Foundation, 2018). To test the robustness of our results, we performed supplementary analyses using principal components analysis of occurrence records (PCA-occ; see  Broennimann et al. , 2012) , where D values were calculated as the proportion of the pooled animal niche rather than the proportion of the background climate information (PCA-env). We also performed supplementary analyses with a stable climate gradient (modern-day WorldClim data: Supporting Information Figure  S4 ). To independently test whether the relative importance of hunting and animal husbandry changed through time, we separately calculated the relative mean contribution of wild and domestic faunal remains for each time interval on the basis of the number of individual specimens (Figure  3b , turquoise line), and the mean absolute number of wild remains.
Therefore, the absence of evidence in our study is not evidence of pastoralist absence. Investigative power can always be increased by analysing a more complete fossil record at higher temporal resolution, incorporating a wider variety of land use evidence, and making continental comparisons. This type of data-driven approach only recently became possible with the collation of continental databases like that of Jousse (2017) for Africa, or those for Europe  (Manning, 2016; Manning, Colledge, Crema, Shennan, & Timpson, 2016). Our methods could be enhanced through the generation of temporally continuous niches rather than discrete time intervals. Radiocarbon date distributions and finer resolution land use changes could be better represented, and comparison with other types of evidence would be simpler.
III. STANDARDIZED NUMERICAL IDENTIFIERS A. What should be a package-level SNI for most prescription drugs? The SNI for most prescription drug packages should be a serialized National Drug Code (sNDC). The   § 201. 17, 201. 18, 211. 130, 211. 137, 610. 60, and 610. 61. ) In addition, the SNI can be linked to databases containing this and other information. Addition of this information within the SNI will unnecessarily increase the length of, and introduce complexity into, the SNI. However, if a manufacturer or repackager chooses to include expiration date and/or lot or batch number with the SNI, it should ensure that the resulting number still permits users to distinguish and make use of the SNI. For example, expiration date and lot or batch number may be incorporated in accordance with the GS1 standards for use of Global Trade Item Numbers (GTIN) 6  (discussed below in Section F).
E. Should the SNI be in human-and machine-readable forms? FDA believes that an SNI generally should be applied to each package in both human-readable and machine-readable forms. However, at this time, FDA is not specifying the means of incorporating the SNI onto the package. The SNIs described in this guidance are compatible with, and flexible for, encoding into a variety of machine-readable forms of data carriers, such as standards organization that works with manufacturers, distributors, retailers, and others in the drug supply chain. A GTIN may be used to uniquely identify items at the package level throughout the supply chain. FDA has been an active observer and participant in GS1 standards development related to healthcare and drug products. According to documentation from GS1, the GTIN is used worldwide by twenty-three industry sectors, including healthcare, and has been adopted by sixty-five countries to uniquely identify pharmaceutical products.
, 2009; Mommert et al. , 2012; Santos-Sanz et al. , 2012; Vilenius et al. , 2012; Bauer et al. , 2013; Lellouch et al. , 2013 Lellouch et al. , , 2017)). The vast majority of TNOs are too faint for spectroscopic studies though, so most of our understanding of TNOs comes from broadband photometric surveys mentioned above  (Brown et al. , 2011b; Peixinho et al. , 2012 Peixinho et al. , , 2015;; Dalle Ore et al. , 2013; Lacerda et al. , 2014). TNOs show a vast variety of physical properties. For example, their albedo varies from a few percent to almost 100% for the largest objects, and colors vary from neutral-blue compared to the Sun to the reddest color observed in the solar system. Interpreting the observational dataset can be challenging and involves a detailed understanding of primordial properties of TNOs and evolutionary processes such as: -Irradiation  (Brunetto et al.
There is also a correlation between the color and the inclination of TNOs  (Tegler and Romanishin, 2000; Hainaut and Delsanti, 2002; Trujillo and Brown, 2002; Doressoundiram et al. , 2005; Peixinho et al. , 2008 Peixinho et al. , , 2015)). The inclination distribution in each color group suggests that these TNOs were formed from different populations of planetesimals in the disk, rather than having suffered from distinct evolution processes such as collisional resurfacing  (Marsset et al. , 2019). As helpful as broadband photometry may be, it remains a proxy in terms of providing constraints of TNOs' surface composition. In this context, it is expected that the next generation of telescopes, such as the James Webb Space Telescope (JWST), will enable the study of TNOs with an unprecedented sensitivity, improving both the quality of our data and the sample of objects studied.
The G140H and G140M gratings are shown for only the F100LP filter combination. This plot was created using the JWST Exposure Time Calculator (ETC) found at jwst. etc. stsci. edu and the following parameters: a point source with a flat continuum set to 10 µJy, the NRSIRS2RAPID readout mode, FULL frame subarray, 67 groups/1 integration/1 exposure (totaling 992 s of photon collection time), the "IFU Nod Off Scene" strategy, a 0. 2" aperture radius, and a high background centered at (12 h , 0 • ). dark material that lowers the albedo of TNOs is still unknown. Organics are suspected to produce the reddest colors observed amongst TNOs, but the lack of high quality data for many objects has prevented a strong confirmation. These compounds, however, have different diagnostic absorption features beyond 3 microns, which will be accessible from NIRSpec.