dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatial, Hydrological, and Geochemical Characteristics The majority of OFG bodies in our database share the following characteristics: (i) They occur within 55 km of the coast and down to a water depth of 100 m, (ii) the top is located between 0 and 200 m below the seafloor, and (iii) they comprise multiple OFG bodies that collectively are <1 km thick (Figure  2  and Table  2 ). OFG has been documented in most continental margins, with 73% of the records located in passive margins and 27% in active margins (Table  2 ). Passive margins tend to have thicker OFG bodies, whereas active margins have a larger fraction of OFG bodies in deeper waters. The deepest OFG occurrences in terms of seafloor depth (>1,750 m; n = 15) are predominantly in convergent margins, where they have minimum salinities of >15 PSU and were emplaced by diagenesis and/or gas hydrate decomposition.
Global Volume of OFG We have used the database  (Micallef, 2020)  and the approach described in the supporting information to calculate the global volume of OFG at 1,094,000 km 3. This is slightly higher than earlier estimates but on the same order of magnitude  (Adkins et al. , 2002; D. Cohen et al. , 2010; Post, Groen, et al. , 2013). The actual OFG volume is expected to be larger because the records in the database are not always located in the distalmost part of the OFG bodies, and we have only considered records where the emplacement mechanism is meteoric recharge. Almost 20% of the calculated OFG volume (212,000 km 3 ) is estimated to have a salinity of <10 PSU.
, 2020; Evans, 2007; Evans & Lizarralde, 2011; Gustafson et al. , 2019; Micallef et al. , 2020; H. Müller et al. , 2011; Mulligan et al. , 2007; Russoniello et al. , 2013) , shore-based or nearshore regions  (Dimova et al. , 2012; Haroon, Lippert, et al. , 2018; Levi et al. , 2018; Lippert & Tezkan, 2020) , and shoreline-crossing regions with airborne methods  (Siemon et al. , 2009). Here, we focus on high-resolution, marine controlled-source electromagnetic (CSEM) methods, with a review of the principles, instrumentation, and data interpretation. Additional electrical resistivity background measurements to larger depths can be obtained using the magnetotelluric (MT) method. The MT method uses natural EM waves as a source signal, which are generated by the interaction of the Earth's magnetic field with the sun's radiation and plasma emissions, and through lightning activity.
Drilling, Coring, and Logging Scientific boreholes are still relatively rare in the upper few hundred meters of continental shelves due to challenges with drilling and sampling (Figure  1 ). The record in industry boreholes, on the other hand, generally starts hundreds of meters below the seafloor, missing most OFG bodies, and access may be limited by proprietary issues. Most drilling campaigns have implemented data acquisition programs that are not directly relevant to OFG studies. Where drill sites intersect OFG systems  (Micallef, 2020)  and appropriate data are available, additional insights may be gained with respect to the physical properties of aquifers and the geochemical structure of OFG. No dedicated OFG drilling project has been completed to date through Ocean Drilling Program (ODP)/ IODP.
Nuclear magnetic resonance logs, on the other hand, can determine rock porosity, fluid content and permeability. The significant amount of seismic reflection data already acquired for hydrocarbon exploration on continental margins worldwide, where available for shallow sub-seafloor depths, provides a valuable database for locating and investigating OFG bodies, as shown in Australia and Suriname  (Groen, Velstra, & Meesters, 2000; Varma & Michael, 2012). The application of techniques used in petroleum geology can ensure that the full potential of the seismic reflection data is exploited. Pre-and post-stack seismic attributes can be used to interpret lithology and provide quantitative estimates of porosity, which can be related to permeability in sub-surface rocks. Seismic inversion is commonly used in hydrocarbon exploration to transform seismic reflection data into quantitative rock properties  (Avseth et al.
, 2001). However, to our knowledge, contained OFG bodies have not been directly or intentionally exploited so far. Whether OFG can be used as an unconventional source of potable water in coastal regions is still uncertain. This is mainly due to difficulties with prospecting and quantification, and a resulting lack of information on the location, volume, and salinity of the OFG, and the properties of the aquifer where it is hosted and the overlying confining units. These difficulties can be addressed as described in section 4. Here we use the available information in the database  (Micallef, 2020)  to carry out a simple calculation of where OFG may be a source of freshwater and for how long. We consider those records where OFG is meteorically recharged, has a minimum salinity of <10 PSU, and is hosted in sandstone aquifers.
Note that both variables appear in each of the equations. Steady-state forms of these equations are given by: ∂ ∂x T f x ∂h ∂x ¼ R -Q p (A23a) ∂ ∂x T s x ρ s -ρ f ρ s ∂z ∂x þ T s x ρ f ρ s ∂h ∂x ¼ 0 (A23b) Figure  A6  compares steady-state solutions with the sharp-interface (Equations A23a and A23b) equations, finite element solutions, variable-density groundwater flow (Equation  A1 ), and solute transport (Equation  A7 ) equations. The parameters used in this model inter-comparison are listed in Table  A1. The steady state sharp-interface equations were solved numerically using MATLAB. The two unknowns were solved for simultaneously using the finite difference method. About six iterations were required to converge at a solution. The maximum water table height was about 10. 5 m above sea level along the right side of the model.
Acknowledgments This study has received funding from the  European Research Council (ERC) , under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 677898  (MARCAN) to A. M. ) and the  U. S. National Science Foundation (NSF   FRES 1925974  to M. P. ;  NSF   OCE 0824368  to B. D. ; and  NSF   EAR 1151733  to H. A. M. ). T. M. , B. W. and Y. Z. were funded by the  SMART  project through the  Helmholtz European Partnering Initiative  (Project ID Number  PIE-0004 ) involving  GEOMAR  and the  University of Malta.
Water body selection In total, 30 ponds and 4 lakes were studied for their limnology, biogeochemistry, and fluxes of CO 2 and CH 4. Some lakes and ponds have been sampled sporadically since 2008, but the majority of the data presented here is from four consecutive summers  (2014) (2015) (2016) (2017) , in 2015 starting late May and in 2016 extending until mid-August. The chosen water bodies are well represented at the study site (Fig. 1 ). Two of the chosen lakes (Table  2 ) were formed in kettle basins and are mainly surrounded by inorganic glaciofluvial deposits and till. This type of glacial lake is common across the Arctic  (Wik et al. 2016a ). Since they fill depressions left by melted glacial ice, they tend to be deeper (> 5 m) than other types of freshwater bodies in the area. The valley also contains several thermokarst lakes formed within the above-mentioned organic-rich peat-loess complex.
Limnological data The thermal structure of a few ponds and lakes (BYL27, BYL37, BYL66, and BYL80) was monitored for a minimum of 2 years (5-s to 1-h intervals) using submersible temperature loggers (RBR SoloT, Vemco Minilog-II-T, or Hobo U12, depending on the year and water body; specifications for all sensors are given in Table  S1 ). In addition to these loggers recording annual cycles, manual measurements of temperature and dissolved oxygen (DO; ProODO, YSI Inc. ) were carried out on all studied lakes and ponds with varying frequency during the field seasons. Limnological seasons were defined based on changes in the thermal structure of the water column (Table  3 ), also using seasonal patterns in DO, CO 2 , and CH 4 to validate and adjust the limits of these periods. The length and timing of ice-out, mixing, and stratified periods can differ by 1-10 days between individual water bodies and years; hence for simplicity, these seasons are differentiated only between lakes and ponds.
Methane production and oxidation The stable isotopic composition of CH 4 (dissolved in surface waters or emitted through ebullition) as well as the 13 C isotopic fractionation factor between CH 4 and CO 2 (α c = 1. 040-1. 055) both imply that acetoclastic methanogenesis is the major CH 4 production pathway in Bylot ponds and lakes (Fig. 10 ;  Whiticar et al. 1986 ). However, as the majority of stable isotope data were collected in July, it is possible that the lower availability of labile organic compounds in earlier or later months might switch prevailing methanogenesis to hydrogenotrophy (CO 2 reducing; Alstad and Whiticar 2011). The hydrogenotrophic methanogens were indeed identified as equally abundant in genomic analyses of pond sediments at the study site  (Negandhi et al. 2013). A portion of produced CH 4 can be consumed by methanotrophs before being emitted to the atmosphere, and an active methanotrophy has been associated with brown mosses for coalescent ponds (CP), ice-wedge trough ponds (IWT), kettle lake (BYL36), thermokarst lake (BYL66), and BYL66 sediment pore water (lake sediment), after  Whiticar et al.
Acknowledgments We are grateful to  Parks Canada , the  Centre for Northern Studies (CEN)  and  G. Gauthier  for helping with logistics during fieldwork,  A. Veillette ,  M. Tremblay ,  Y. Seyer ,  T. Pacoureau  and  F. Mazoyer  for their assistance with collecting and processing of the samples,  M. Bartosiewicz  and  S. MacIntyre  for advice on data analyses, and  S. Duval  and  T. Hao Bui  for gas chromatography and stable isotope analyses. The study was funded by the  Natural Sciences and Engineering Research Council Discovery program ,  Network of Centres of Excellence ArcticNet ,  Natural Resources Canada Polar Continental Shelf Program ,  International Polar Year , and  Fonds de recherche du Québec-Nature  et technologies team grant (IL), as well as the  CREATE program  EnviroNorth (VP) and the  W.
Field sampling Water samples were collected from 4 stations during 2 cruises in the SCS aboard the R/V Tan Kah Kee in August 2018 (KK1806) and July 2019 (KK1905; Fig. 1 ). Salinity, temperature, dissolved oxygen (uncalibrated, performed to observe vertical trends), fluorescence, and photosynthetically active radiation (PAR) data were obtained via a SeaBird conductivitytemperature-depth (CTD) profiler (SBE 911 plus). Water samples for incubations were obtained from a rosette of 24 × 12 L Niskin bottles.
Cell abundance and mean diameter Cell abundance and mean diameter data were obtained using a FACSAria flow cytometer following  Jiao et al. (2005). 1. 98 mL of seawater were collected just before the PN sampling at each time point, and fixed with 20 μL 50% glutaraldehyde. After mixing, samples were incubated in the dark for 15 min before being flash-frozen in liquid N2. Samples were then stored at -80° C until further analysis. Before analysis, samples were thawed and stained with SYBR Green I (Invitrogen). Beads (1 μm) were added as an internal standard. Green fluorescence (FITC-A) and side scatter (SSC-A) were used to count cells using FlowJo software. Mean cell diameter was calculated from side scatter (SSC-A) data using a linear regression model of heterotrophs  (Calvo-Díaz and Morán, 2006).
Data compilation We compiled data from 17 published ANF rate studies (Table  2 ) including location, depth range, volumetric and depthintegrated N2 fixation rates, aphotic zone N2 fixation contribution, and method used for rate determination. These studies covered a wide geographic range, from coastal and marginal seas to open oceans. Only N2 fixation data below the euphotic zone were included. For studies that did not report euphotic zone depth, only data below 200 m were compiled. In order to resolve the relationship between observed detectable ANF, SNFR and PN, we only compiled values collected from depths below 200 m. We also discarded studies reporting too few data. For depths without SNFR data, SNFR were derived by dividing ANF by PN. In this study, 13 out of 18 ANF depths were compiled (Southern California Bight (SCB): 14 of 21 ANF depths  (Hamersley et al.
Interestingly, SCB, Mediterranean Sea and WTSP showed significant correlations between PN concentration and SNFR with varied negative slopes (Fig. 10 c  and Table  S1 ). This might be due to increasing non-diazotrophs with increasing PN. Considering PN decreases exponentially with depth, niche separation caused by vertical gradients of environmental factors may also be an underlying reason. Still, more studies are needed to further indicate global patterns and controlling factors of ANF. It is worth highlighting that complete dataset of PN, SNFR and ANF are essential to discuss their correlations and further examine the applicability of PN as a predictive proxy for ANF.
In some regions, detectable ANF had significant correlations with PN concentration, suggesting that easily measured PN could be a regional predictive proxy for ANF in the deep ocean. Based on above trials and results, we here list several recommendations for future ANF studies: 1) In order to detect low ANF rates, we recommend the use of the persulfate-denitrifier method to measure δ 15 N-PN when sufficient PN mass for the EA-IRMS approach cannot be achieved. 2) Both higher spatial and temporal resolution and corresponding complete datasets of PN, SNFR and ANF are needed to better constrain the controlling factors of ANF and the contribution of ANF to the global N budget. 3) Further rate studies coupled to molecular approach such as nano-SIMS coupled to catalyzed reporter deposition fluorescence in situ hybridization (CARD-FISH) are needed to bridge the knowledge gap between ANF rates and diazotrophs in the deep ocean.
Acknowledgements This study was supported by the  National Natural Science Foundation of China (NSFC  no. 92058204 ,  41721005 ). Many thanks to all the cruise member on R/V Tan Kah Kee during Aug 2018 and Jul 2017 cruises ( KK1806  and  KK1905  cruises). Special thanks to  Xirong Chen  for his help during the experiment,  Xiaowei Chen  for his help on flow cytometry data analysis,  Zhixiong Huang  for EA-IRMS standard data,  Wenbin Zou  and  Li Tian  for experimental helps.
Mapping the deposits and the stream catchments The areas of the sampled mass wasting deposits are mapped through ArcGIS using the detailed GPS location recorded during the field survey, and the satellite image of ArcGIS World Imagery. The surface covered by fresh rocks on the deposits are usually much brighter than the surface colonized by vegetation including lichen. We mapped the area colonized by vegetation on each deposit and calculated the vegetation coverage (Table  1 ). For the sampled streams, we extract their catchments with the Hydrology tools in ArcGIS based on the 30-m resolution ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model) data (Geospatial Data Cloud, htt p://www. gscloud. cn/), and for streams that is too small to be extracted, the catchments were mapped on ArcGIS by hands.
Comparison with alpine weathering of granitoids The particularity of the periglacial weathering in the North Chayu can be further supported by the comparison with the large dataset of granitoid watersheds  (White and Blum, 1995; Oliva et al. , 2003; Gurumurthy et al. , 2012; Wu et al. , 2013; Zhang et al. , 2016; Sun et al. , 2019). For a direct comparison, that dataset has been corrected for cyclic input following the same method used for the Chayu catchment. The K*/Si and Ca*/Si ratios do not correlate with MAT, where MAT has been estimated using a local thermal gradient of -6. 1 • /km and the mean altitude of the corresponding catchment (Fig. 6 ). The data span a MAT range of 0 to 30 • C, and their K*/Si ratios are <0. 37 and Ca*/Si ratios are <2. 33. A watershed with a K*/Si of 0. 66 corresponds to very high [K + ] of 212 μmol/L in river water, and such values are likely to witness a significant contribution from fertilizer  (Sun et al.
, 2019)  and are not considered in the dataset. So with these, a rough range of water compositions draining granitoid lithology under most of the environmental conditions on the earth surface is defined. The data from the South Chayu streams are compatible to this range (Fig. 6a, b ). In contrast, the North Chayu data, draining mostly granitoid lithology as well, are much higher, highlighting the effect of its periglacial environment. Although the North Chayu is distinctive to all the other watersheds in the world granitoid dataset, we regard that the alpine periglacial environment is not rare on the Earth surface, but are only rarely studied. The alpine periglacial environment should draw attention since it nurtures the headwaters of most mountain rivers.
Acknowledgement This work was supported by the  Second Tibetan Plateau Scientific Expedition and Research (STEP) program  (Grant No. 2019QZKK0707 ). We thank  Yibo Yang  and  Chengcheng Ye  for their assistance in the field,  Bernhard Zimmermann  and  Aimeryc Schumacher  for their assistance in elemental analysis and  Jérôme Lavé  for discussions on the morphology of landslides. Thanks to  Niels Hovius  and  Christoff Andermann  for the access to their analytical facilities.
Further details on the operating conditions are provided in Table  S3  in the Supplement. Glass from the rhyolitic Lipari obsidian and the basaltic Laki 1783 CE tephra was analysed as secondary standards during the analytical period and compared to recommended values from  Kuehn et al. (2011). A sample of V1477 tephra from Suðurárbotnar, northern Iceland (G. Guðrún Larsen, personal communication, 2020) , was analysed as a QUB Veiðivötn internal standard. The raw sample and secondary standard analyses are provided in the Supplement (Tables  S1  and S2 ). Geochemical analyses from several occurrences of the V1477 tephra were collated for data analysis (Fig. 2 ; Table  S4 ). To facilitate graphical and statistical comparisons between all datasets the geochemical data were normalised to an anhydrous basis (i.
Impact of volcanic eruptions on Northern Hemisphere summer temperatures In addition to the ice-core and tephra analysis, we quantified the cooling induced by the V1477 eruption using the NVOLC v2 dataset of  Guillet et al. (2020)  and compared it to other major Icelandic eruptions of the Common Era. The NVOLC v2 dataset includes 13 tree-ring width (TRW) and 12 maximum latewood density (MXD) chronologies spread across the Northern Hemisphere (NH; for further details see  Stoffel et al. , 2015, and Guillet et al. , 2017). All chronologies in NVOLC v2 have either been calibrated against regional instrumental climate data and transferred into temperature units or have been interpreted by the original authors as a temperature proxy. Here, a principal component regression (PCR) approach was chosen to transfer TRW and MXD data into NH summer (or JJA for June-July-August) temperature units, expressed as anomalies with respect to the 1961-1990 reference period.
A reduced space signal of the proxy records was then extracted using principal component analysis (PCA), resulting in a set of principal component (PC) scores and PC loading patterns. The first n PCs with eigenvalues > 1 were retained as predictors to develop a multiple linear regression model. A multiple cross-validation using random calibration sets (bootstrapping) was applied to the PCR to estimate the skill of the reconstruction and confidence intervals around the reconstructed anomalies. Because each chronology had different lengths, an iterative nesting method was used to develop the temperature reconstruction. This procedure entails the subsetting of the original dataset into complete data matrices without missing values, so-called nests. In NVOLC v2, 23 nests have been adjusted to the common period of all series.
The earliest spans the period 500-551 CE and is composed of six chronologies. The most replicated nest  (1230 ( -1972 CE) CE)  included 25 chronologies. The nested PCR was computed schematically following a threestep procedure. In each nest, the number of predictor variables was first reduced using a principal component analysis; secondly, the PCs with eigenvalues > 1 were retained as independent variables within ordinary least-square (OLS) multiple regression models, while a mean NH JJA temperature series, obtained from the Berkeley Earth Surface Temperature (BEST) dataset over the period 1805-1972 CE, was used as a dependent variable. The robustness of each model was tested based on a traditional split calibration-verification procedure bootstrapped 1000 times, and the final reconstruction of each nest was computed as the median of the 1000 realisations given with their 95 % confidence interval.
95 and the vast majority > 0. 97 (Table  S6 ). In contrast, comparisons between TUNU2013 78. 655 m and the V1477 occurrences range between 0. 917 and 0. 957, values typically thought to indicate that the tephras originate from the same volcanic source but not the same eruption (Table  S6 ;  Begét et al. , 1992; Abbott et al. , 2018). Geochemical offsets can result from tephra shards with the same composition being analysed under contrasting operating conditions on different microprobes and/or at different times  (Kuehn et al. , 2011). However, a comparison of secondary standard analyses from the analytical periods for the TUNU2013 shards and those from the Skaftártunga occurrence of V1477  (Streeter and Dugmore, 2014)  strongly indicates that the geochemical offsets are not the result of analytical issues but are real differences in composition between the sample datasets (Fig.
The individual L2FP quality flags are applied for both GOSAT and OCO-2 during the collocation procedure, and then the mean value of XCO 2 from the 10 to 100 collocated OCO-2 soundings is calculated and subtracted from the corresponding GOSAT XCO 2 to produce XCO OCO-2   2. Here we compare ACOS GOSAT v9 against OCO-2 v10 (rather than to the deprecated v9), since we assume that science users will adopt the newest OCO-2 product. Major updates to the version 10 ACOS L2FP algorithm  (Osterman et al. , 2020)  are discussed in Sect. 3. 1. One complexity in comparing ACOS GOSAT v9 and OCO-2 v10 is the fact that the two versions of the algorithm used different CO 2 priors. Typically, models which assimilate satellite CO 2 data take into account the unmeasured part of the prior CO 2 profile (specified via the retrieval's averaging kernel) via an averaging kernel correction, as given in Eq.
(1). Therefore, in order to fairly compare these two data sets as models would assimilate them, we need to remove their difference due to the unmeasured part of the CO 2 profile, as follows: XCO 2 = XCO 2 + 20 i=1 h i (1 -a i ) • (u a,i -u a,i ), ( 3 ) where h is the XCO 2 pressure weighting function, a is the normalized XCO 2 averaging kernel, u a is the ACOS v9 CO 2 prior profile used for GOSAT, and u a is the ACOS v10 CO 2 prior profile used for OCO-2. The summation takes place over the 20 vertical levels defined in the ACOS code. In summary, the total adjustment to the ACOS GOSAT XCO 2 value is calculated as the contribution of the difference in the vertical CO 2 priors at each level weighted by the one minus the averaging kernel at that level. The global mean adjustment due to the CO 2 prior correction was approximately 0.