dataesr/scientific-paragraphs-categorization huggingface dataset:
The food enzyme considered in this opinion is a glucan 1,4-a-maltohydrolase (maltogenic a-amylase; EC 3. 2. 1. 133) produced with the genetically modified Bacillus subtilis strain MAM by the company DSM Food Specialties B. V. The food enzyme contains neither the production microorganism nor recombinant DNA; therefore, no environmental risk assessment is required. However, the Panel emphasises that this conclusion only covers the food enzyme recovered via filter press. The glucan 1,4-a-maltohydrolase is intended for use in baking processes. Based on the maximum use levels recommended and individual consumption data from the EFSA Comprehensive European Food Consumption Database, dietary exposure to the food enzyme-total organic solids (TOS) was estimated to be up to 0. 175 mg TOS/kg body weight (bw) per day in European populations.
Chemical parameters The glucan 1,4-a-maltohydrolase (maltogenic a-amylase) produced with the genetically modified B. subtilis strain MAM consists of a single polypeptide of 719 amino acids, including a signal peptide of 33 amino acids, which is cleaved off during secretion of the enzyme protein. The molecular mass of the enzyme protein of 66 kDa was estimated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis, which was also used to investigate the protein homogeneity of the food enzyme. The gels presented showed one main protein band and some minor protein bands of lower molecular mass. No enzymatic side activities have been reported by the applicant. Data on the chemical parameters and the protein homogeneity of the food enzyme were provided for four food enzyme batches, three batches to be used for commercialisation and one batch used for the toxicological tests (Table  1 ).
Manufacturing process The food enzyme is manufactured according to the Food Hygiene Regulation (EC) No 852/2004 4  and in accordance with current Good Manufacturing Practice (GMP). The manufacturing process is certified according to Food Safety Systems Certification 22000 (FSSC 22000). A data set related to the manufacturing process including a list of raw materials used and a production flow process from fermentation and downstream processes was provided. The food enzyme is produced by a pure culture of B. subtilis MAM in a contained, submerged, fedbatch fermentation system with conventional process controls in place. The identity and purity of the culture are checked at each transfer step from frozen vials to the end of fermentation. The downstream processing includes a pretreatment, recovery, purification and concentration.
EFSA Comprehensive European Food Consumption Database Since 2010, the EFSA Comprehensive European Food Consumption Database (hereafter the EFSA Comprehensive Database 7  ) has been populated with detailed national data on food consumption. Competent authorities in European countries provide EFSA with data regarding the level of food consumption by individual consumers, as taken from the most recent national dietary survey in their country (EFSA, 2011a). The food consumption data gathered by EFSA were collected using different methodologies and thus direct country-to-country comparisons should be made with caution. Depending on the food category and the level of detail used in exposure calculations, uncertainties might be introduced owing to subjects possibly underreporting and/or misreporting of consumption amounts.
Nevertheless, the EFSA Comprehensive Database is the best available source of food consumption data across Europe. Food consumption data from the population groups: infants, toddlers, children, adolescents, adults and the elderly were used for the exposure assessment. For the present assessment, food consumption data were available from 33 different dietary surveys carried out in 19 European countries (Appendix A). Consumption records were codified according to the FoodEx classification system (EFSA, 2011b).
Exposure to food enzyme-TOS according to the intended use proposed by the applicant Exposure to the food enzyme-TOS was based on intended use and the recommended maximum use levels of the food enzyme-TOS provided by the applicant (Section 3. 1. 8). Food enzyme-TOS exposure was calculated from foods produced involving a baking process. Relevant food groups and/or individual foods were selected from the Comprehensive Database and were assumed to always contain the food enzyme-TOS at the maximum recommended use level. This will result in an overestimation of exposure to food enzyme-TOS. To facilitate matching of the reported use levels for baking processes with foods identified in the Comprehensive Database, the selected foods were disaggregated to ingredient level as appropriate, and converted into the corresponding raw material, i.
e. flour, via the application of conversion factors (Appendix B). For example, consumption of 100 g of bread was converted into an intake of 70 g flour (recipe fraction of 0. 7) and then multiplied by 15. 6 mg TOS/kg flour, as provided by the applicant, to arrive at an exposure of 1. 09 mg TOS/100 g bread. Exposure to the food enzyme-TOS was calculated by multiplying values reported for each food category by their respective consumption amount per kilogram of body weight (kg bw) separately for each individual in the database. Table  2  provides an overview of the derived exposure estimates. The average and 95th percentile exposure to the food enzyme-TOS per age class, country and survey are reported in Appendix C -Table  C. 1. The contribution of the food enzyme-TOS from each FoodEx category to the total dietary exposure is indicated in Appendix C -Table  C.
Upon treatment with the food enzyme, there was no increase in revertant colony numbers above control values in any of the S. Typhimurium strains, with or without metabolic activation. In E. coli WP 2 uvrA, a reproducible, dose-related increase in the number of revertants was observed in the absence of S9-mix, with values above the historical control data range at 3,330 lg/plate, corresponding to 236 lg TOS/plate. Compared to the solvent control, 1. 9-and 2. 1-fold increase in the number of revertants was recorded at this concentration in two experiments. These results could be due to the concentrations of tryptophan in the test item, exceeding the critical concentrations used in the plate incorporation assay and thus leading to a false positive outcome. To rule out this possibility, the applicant was invited to repeat the test applying the 'treat and plate' assay.
The SeaSWIR dataset consists of 137 ASD (Analytical Spectral Devices, Inc. ) marine reflectances, 137 total suspended matter (TSM) measurements and 97 turbidity measurements gathered at three turbid estuarine sites (Gironde, La Plata, Scheldt). The dataset is valuable because of the high-quality measurements of the marine reflectance in the Short Wave InfraRed I region (SWIR-I: 1000-1200 nm) and SWIR-II (1200-1300 nm) and because of the wide range of TSM concentrations from 48 up to 1400 mg L -1. The ASD measurements were gathered using a detailed measurement protocol and were subjected to a strict quality control. The SeaSWIR marine reflectance is characterized by low reflectance at short wavelengths (< 450 nm), peak reflectance values between 600 and 720 nm and significant contributions in the near-infrared (NIR) and SWIR-I parts of the spectrum.
Introduction The SeaSWIR dataset (https://doi. org/10. 1594/PANGAEA. 886287) has been collected in the framework of the SeaSWIR project (Remote sensing of turbid waters in the Short Wave Infrared) to determine the variability of the marine reflectance in the short-wave infrared (SWIR, 1000-2500 nm) as a function of total suspended matter (TSM) concentration and turbidity (T ). In  Knaeps et al. (2015)  the SWIR region between 1000 and 1300 nm was divided into two sub-regions based on the shape and magnitude of the pure water absorption. The SWIR-I region ranges from 1000 to 1200 nm, close to a local maximum in the pure water absorption. The SWIR-II region ranges from 1200 to 1300 nm, where the pure water absorption reaches a value of 132. 2 m -1. Beyond 1300 nm the pure water absorption is extremely high (> 140 m -1 ) and there is no reflectance emerging from the water.
The marine reflectance in the SWIR has not been reported before because specialized optical instrumentation is required to measure in the SWIR. More information on the SWIR marine reflectance is essential because several satellites have sensors with spectral bands in this range. Examples include Sentinel-3 OLCI with a spectral band at 1020 nm, MODIS AQUA and TERRA with spectral bands at 1240, 1640 and 2130 nm, and the Visible Infrared Imaging Radiometer Suite (VIIRS) with spectral bands at 1240, 1378, 1610 and 2250 nm. Knowledge of the marine reflectance in these spectral bands will be used first of all to underpin or refute the assumptions of a black pixel (often assumed in at- mospheric correction over turbid waters). However, earlier publications using this dataset  (Knaeps et al. , 2012 (Knaeps et al.
, , 2015) )  have also shown the existence of a relationship between the SWIR marine reflectance at 1020 and 1070 nm and the TSM concentrations and have proposed a TSM algorithm for the SWIR bands. Such a SWIR TSM algorithm is particularly useful in coastal waters with extremely high turbidity where shorter bands in the NIR saturate. To measure marine reflectance in the SWIR-I and SWIR-II, a typical "land" ASD (Analytical Spectral Devices, Inc. ) spectroradiometer (FieldSpec) was used, recording data from 350 to 2500 nm. A measurement methodology was developed for the ASD to derive the marine reflectance from this single radiometer system and results were compared with marine reflectance measured using a three-radiometer system (TRIOS). As the pure water absorption is high in the SWIR, only water with a high concentration of suspended particles will have a measurable reflectance.
Therefore, test sites were chosen with high to extremely high turbidity: the Scheldt River in Belgium, the Gironde estuary in France and Río de la Plata estuary in Argentina (Fig. 1 ). Measurement campaigns were organized at all three sites, mainly on fixed pontoons (floating dock) along the rivers/estuaries. The measurement campaigns resulted in a large dataset of 137 ASD reflectance spectra in the visible/near-infrared (VNIR) and SWIR with corresponding TSM concentrations and turbidity. This paper describes the methodology for data acquisition and processing and analyzes the final reflectance, TSM concentrations and turbidity values. Additional quality control of the ASD reflectance is performed with simultaneously acquired TRIOS reflectance data.
Sites Field data have been collected in three different estuaries with high to extremely high TSM concentrations: the Scheldt in Belgium, the Gironde in France and the Río de la Plata in Argentina (Fig. 1 ). These three estuaries were chosen be-cause of their high concentration range of suspended particles and diversity of particle composition/type. It was expected that the high concentration of TSM would result in a measurable water reflectance signal in the SWIR. The Scheldt River has its origin in France, flows through Belgium and reaches the North Sea in the Netherlands. It forms a macrotidal estuary leading to TSM concentrations within surface waters varying from a few to several hundreds of mg L -1 during one tidal cycle. The study area is located near the city and the harbor of Antwerp where there is a tidedominated Maximum Turbidity Zone (MTZ).
High values of TSM have been reported in this region, with mean values ranging from 100 to 300 mg L -1 and extreme concentrations around 1000 mg L -1 in the Maximum Turbidity Zone  (Dogliotti et al. , 2014). This estuary has been identified by a number of investigators  (Shi and Wang, 2009; Doerffer, 2006; Moore et al. , 2010; Doron et al. , 2011)  as a very bright target which can be used for evaluating the performance of atmospheric correction algorithms, so far tested without any in situ data  (Dogliotti et al. , 2011). Field sampling was performed on the Club de Pescadores Pier in Buenos Aires in 2012 (Table  1 ). Most of the measurements from the SeaSWIR dataset were collected from fixed pontoons along the sides of the rivers/estuaries. These pontoons allow measurement of a large variation of TSM concentration and T during the daily tidal cycle and allow us to make high-quality reflectance measurements.
6 % of the full scale used for difference between measurements), the complete sample is removed. A large difference in E d indicates highly variable light conditions during the measurements. -The standard deviation of R w1 , R w2 and R w3 at 750 nm is calculated. When R w_SD (750) > 0. 01 the measurement is discarded. -After subtracting 1350 nm an inspection is performed of remaining reflection between 1500 and 1700 nm which could be an indication of problems with skylight reflection. All measurements in this wavelength range with R w > 0. 005 are removed. The SeaSWIR dataset contains all water reflectance measurements retained after quality control, as well as the downwelling irradiance (average of E d1 (0+), E d2 (0+), E d3 (0+)), the downwelling sky radiance (average of L sky1 (λ), L sky2 (λ), L sky3 (λ), L sky4 (λ), L sky5 (λ), L sky6 (λ), L sky7 (λ), L sky8 (λ), L sky9 (λ)) the total upwelling radiance (average of L sky1 (λ), L sky2 (λ), L sky3 (λ), L sky4 (λ), L sky5 (λ), L sky6 (λ), L sky7 (λ), L sky8 (λ), L sky9 (λ) ) and all standard deviations.
The sampling interval of the TRIOS is approximately 3. 3 nm and the spectral width is about 10 nm. Position is measured simultaneously by GPS. Data were acquired with the MSDA-XE software and radiometrically calibrated using nominal calibration constants. Calibrated data for E 0+ d , L 0+ sea , and L 0+ sky are interpolated to 2. 5 nm intervals and exported to MATLAB or R for further processing. Data are recalibrated using calibration updates from annual laboratory or factory calibrations. Full details of the data processing, including scan selection and averaging and quality control, are described in  Ruddick et al. (2006) , including its Web Appendix 1, except that a fixed value of 0. 0256 is used for the air-sea interface reflection coefficient in sunny conditions, instead of the wind speed formulation, because of the fetch-limited surface wave field, as explained previously for the ASD data in Sect.
ASD and TRIOS reflectance The SeaSWIR reflectance dataset is generally characterized by low reflectance values at short wavelengths (< 450 nm), peak reflectance values between 600 and 720 nm and significant contributions in the NIR and SWIR-I parts of the spectrum -see Fig. 5. However, significant variations exist among the different test sites. To further explain these differences, the water reflectance spectra for all data campaigns are displayed in Fig. 5  from 350 to 1300 nm. Beyond 1300 nm the pure water absorption is extremely high (> 140 m -1 ) and there is no reflectance emerging from the water. In Fig. 5 , five characteristic spectral regions are defined between 350 and 1300 nm: (A) between 350 and 600 nm, (B) between 600 and 720 nm, (C) in the NIR between 720 and 1000 nm, and finally regions corresponding to (D) SWIR-I (1000-1200 nm) and (E) SWIR-II.
The spectra for the Scheldt (data campaigns Scheldt2010 and Scheldt2012, Fig. 5a  and b ) have overall high reflectance values. In region A the spectra have a similar slope related to light absorption of CDOM and non-algal particles. In region B, maximum reflectance values are found of up to 0. 1 and the spectra show clear changes in shape and magnitude. The influence of light absorption by chlorophyll a, for example, is clearly noticeable around 665 nm in the Scheldt2012 dataset. In region C, there is more variability in the magnitude of the spectra more related to the concentration of particles, while spectral variability is determined by pure water absorption  (Ruddick et al. , 2006). In SWIR-I a significant signal can be observed with maxima around 0. 02 (at 1071 nm). In SWIR-II the maximum reflectance is 0.
001 at 1268 nm. The shape of the water reflectance spectrum in the SWIR is also clearly influenced by pure water absorption  (Knaeps et al. , 2012). The spectra for La Plata are shown in Fig. 5e. All the spectra have a very similar shape and have only small differences in magnitude. Maximum reflectance values are found around 700 nm with values of up to 0. 1. There is also a local minimum around 665 nm probably related to chlorophyll a absorption. There is a very weak signal in SWIR-I (maximum 0. 007 at 1071 nm), and no significant signal in SWIR-II. The Gironde spectra (data campaigns Gironde2012 and Gironde2013, Fig. 5c  and d ) have extremely high values, up to 0. 2 around 700 nm. Also here, a saturation effect (little or no increase in reflectance with increasing TSM concentrations) is observed at the shorter wavelengths in region A, followed by a spectral region with high reflectance (B) with maximum values at 500 and 700 nm.
Conclusions The SeaSWIR dataset presents the first hyperspectral measurements of the water reflectance in the SWIR. Measuring  water reflectance in the SWIR was made possible by using an ASD spectrometer with one radiometer and a measurement protocol where the instrument is turned facing the sky, the water and the Spectralon panel. Using this measurement protocol and consecutive post-processing it was shown, by intercomparison with TRIOS measurements, that highquality water reflectance measurements can be made with the ASD. It is however suggested to strictly follow the same procedure as outlined in this paper and apply a strict quality control. Measurements which passed quality control were all performed under relatively constant atmospheric conditions, being blue sky or completely overcast.
Variable light condi- tions prohibit a correct retrieval of the water reflectance as lighting conditions change while turning the instrument and making the three individual measurements of downwelling irradiance, the total upwelling radiance and downwelling sky radiance. It should also be noted that most measurements were made from fixed pontoons, which simplifies the measurement significantly and improves the quality of the data. Still, even when measuring on a pontoon, the location on the pontoon should be chosen wisely and should not be close to any large obstacles which could alter the measurements of the downwelling irradiance and downwelling sky radiance. The quality of this dataset opens up the possibility of including the ASD in future measurement campaigns on water, for the development of algorithms as well as for calibration and validation of satellite and airborne observations.
This is important for current (e. g. , Sentinel-3 OLCI) and future (e. g. , Environmental Mapping and Analysis Program -EnMAP, Hyperspectral Precursor and Application Mission -PRISMA and Sentinel-10) satellite missions with more spectral bands in the SWIR-I and SWIR-II. Next to the water reflectance measurements, the SeaSWIR dataset also contains simultaneous TSM and turbidity measurements (using a HACH portable turbidity meter). The high quality of these measurements was confirmed by the standard deviation of the triplicates and the duplicate measurements performed in the LOV and VITO laboratories. As all measurements were made in estuaries during the tidal cycle, the TSM range of the SeaSWIR dataset is very high, ranging from 48 mg L -1 up to extreme values of 1400 mg L -1. Hence, the SeaSWIR dataset is not only unique in terms of its spectral content, but also in terms of its concentration range.
5 ), as also confirmed by a number of detailed country case studies (SI sections 6b and 6c). Other factors, not explored here, may contribute to the discrepancy in forest fluxes, such as different forest definitions, legacy effects, data sources and methods  7, 18, 19, 30, 31  (SI section 5). The impact of these factors may be further explored in future updates of our analysis, e. g. by extending the comparison of country data with other datasets (e. g. , ref. 29, 32, 33  ) and including other bookkeeping models  19  and updated DGVMs results. However, it is unlikely that these factors and additional analyses would contradict our main conclusions.
Global models, in contrast, estimate the anthropogenic land flux considering fewer management activities on a smaller managed forest area, and include most of the indirect effects on extant forests in the "residual" land response. A simple post-processing approach, disaggregating global models' results, increases their comparability with GHGIs (Figs. 4  and 5 , SI section 7). While differences in scope, methods and datasets will likely preclude complete reconciliation of global model and GHGI estimates, improvements on both sides can help to better understand and attribute differences. This leads to the specific recommendations below, for both GHGIs and global models. Country GHGIs should provide more transparent and complete information on managed forests, including maps, harvested area, harvest cycle, forest age and if/how indirect and natural effects are included.
Developed countries (UNFCCC Annex I): The following 40 countries are included in this study (Table  SI 4 ): Australia, Belarus, Canada, EU (28 countries), Japan, Kazakhstan, New Zealand, Norway, Russian Federation, Switzerland, Turkey, Ukraine and USA. The 1990-2014 time series of forest CO 2 estimates used in this study (Fig. 4 ) are taken from the GHGIs submitted in 2018  58  , and include the following categories from the LULUCF sector: Forest land (including "forest remaining forest" and "land converted to forest"), Harvested Wood Products and forest fires. Estimates for deforestation are from "forest converted to all other land uses". Although GHGIs include all GHG, here we considered only CO 2 to allow comparability with the other datasets used in this study. The main sources of non-CO 2 forest emissions are forest fire (CH 4 and N 2 O) and emissions associated with the loss of forest soil organic matter (N 2 O).
Dynamic Global Vegetation Models (DGVMs) The IPCC Fifth Assessment Report (AR5)  21  and the Global Carbon Project (GCP)  10  assess land model intercomparisons that have been coordinated by the project "Trends and drivers of the regional-scale sources and sinks of carbon dioxide (TRENDY  24  ; http://dgvm. ceh. ac. uk/node/9). The DGVMs were forced with historical data for climate, atmospheric CO 2 concentration, N deposition, and land cover transitions. Some DGVMs include forest management (e. g. , wood harvest) in the simulations (e. g. , refs. 34, 35, 49 ). The TRENDY v4 models  24  were forced with a reconstruction of the land use, either the HYDE dataset of cropland and pasture distributions  63  , or the LUH-v1  64  dataset, based on HYDE, but providing annual, half-degree, fractional data on land cover distribution, including cropland, pasture, "primary" forests and "secondary" forests, as well as all underlying transitions between land-use states, and including wood harvest and shifting cultivation.
The HYDE data are based on annual FAO statistics of change in agricultural area  65. For the period 2011-2013, the HYDE data set was extrapolated by country for pastures and cropland separately based on the trend in agricultural area over the previous 5 years. The HYDE data set is independent from the data set used in the bookkeeping model  9  , which is based primarily on forest area change statistics. Furthermore, although LUH2-v1 dataset distinguishes forested and non-forested land (based on a separate underlying global model  64  ) and indicates whether land-use changes occur on forested or non-forested land, typically only the changes in agricultural areas are used by the models and are implemented differently within each model (e. g. , an increased cropland fraction in a grid cell can either be at the expense of grassland, or forest, the latter resulting in deforestation; land cover fractions of the non-agricultural land differ between models).