dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020) , which covers broad environmental gradients. However, FLUXNET data originates mostly from lower latitudes (e. g. , only five sites from the Arctic and 34 from the boreal out of 224 global sites in total used in  Tramontana et al. , 2016). This could explain the larger net sink strength in FLUXCOM compared to our predictions. The higher predictive performance of FLUXCOM compared to our prediction might also be explained by the fact that FLUXNET is based on a single flux measurement technique (EC) with standardized filtering, gap-filling, and partitioning procedures. We included chambers to our analysis as they covered conditions that were not covered by the EC network even though we acknowledge that using both chamber and EC measurements, and different partitioning methods for EC, increased the number of different flux measurement techniques and study designs, and may have made the comparison of fluxes across sites more uncertain  (Fox et al.
9 × 10 6 km 2 ). In contrast, the tundra biome was on average a small sink (-13 Tg C yr -1 , from -81 to +62 Tg C yr -1 ; 6. 7 × 10 6 km 2 ) or a small source (+10 g C m -2 yr -1 ), based on our predictions and observations. This ). However, it differs from the source budget (+462 Tg C yr -1 , from +94 to +840 Tg C yr -1 ; 10. 5 × 10 6 km 2 ; wetlands not included) estimated by  Belshe et al. (2013). The divergence of average annual NEE across our and  Belshe et al. (2013)  study is likely explained by our inclusion of fluxes from wetlands, which were on average annual net ecosystem CO 2 sinks (Table  1 ). The discrepancy between our and the McGuire et al. (2012) study can be explained by a 50% increase in new annual tundra source observations in our dataset (see e. g. ,  Celis et al. , 2017; Euskirchen et al.
compiled 1448 cumulative annual and growing season flux values (when chamber measurements were aggregated per site; Figure 1); 82% of the aggregated observations are from EC and 18% are from chambers. Annual and growing season NEE were the most widely reported fluxes in the dataset (Figure 1). Unlike McGuire et al. (2012) F I G U R E 1 Measured median annual (a-c) and growing season (.
Observed and predicted annual fluxes of GPP (gross primary production; a and d), ER (ecosystem respiration; b and e), and NEE (net ecosystem exchange; c and f) based on model fit (a-c) and predictive performance (d-e). Model fit was evaluated by predicting fluxes over the entire model training data, while predictive performance was assessed using a leave-one-site-out cross validation scheme in which each site was iteratively left out from the dataset, and the remaining data were used to predict fluxes for the excluded site. Model fit and predictive performance statistics (r = Pearson's correlation between observed and predicted fluxes, g; Bias = mean absolute bias, h; RMSE = root mean square error, i across annual fluxes and five modeling methods; GLM = generalized linear model; GAM = generalized additive model; GBM = generalized boosted regression tree; RF = random forest; SVM = support vector machine) and their median ensemble (ENS) are shown in subfigures g-i.
Our framework is unique in that it (a) compiles a new dataset of growing season and annual fluxes using EC and chamber data and investigates the drivers of these fluxes; (b) quantifies the performance of different statistical models; and (c) provides the first spatially continuous high-latitude maps of CO 2 fluxes and their uncertainties at high spatial resolution, capturing the inherent spatial heterogeneity in predictors and fluxes and minimizing biases.
g. , describing soil moisture and nutrients or permafrost thaw) to explain fluxes, (d) conduct detailed sensitivity tests of the importance of the flux measurement method, data distribution, and different predictor datasets influencing the budgets, and (e) build models at a finer temporal resolution than annual and growing season, to capture rapidly changing transition periods and bypass issues associated with temporal aggregation and varying definitions of seasons. High-latitude specific models are needed to more accurately monitor current emissions and improve understanding of the role of high-latitude regions in the global carbon cycle, as large changes in carbon cycling are likely in the near future.
Funding information Humboldt Fellowship for Experienced Researchers;  European Commission , Grant/Award Number:  H2020-BG-09-2016  and  727890 ;  Helsingin Yliopisto ;  Jenny ja Antti Wihurin Rahasto ; Väisälä fund; Natural Sciences and  Engineering Research Council of Canada ;  Alfred Kordelinin Säätiö ;  Arctic Challenge  for  Sustainability, Grant/ Award Number :  JPMXD1420318865 ;  Suomen Kulttuurirahasto ;  Netherlands Earth System Science Centre; Korean government , Grant/ Award Number:  KOPRI-PN21011 ,  NRF-2021M1A5A1065679 ,  NRF-2021M1A5A1065425  and  NRF-2018R1D1A1B07047778 ;  Gordon and Betty Moore Foundation , Grant/Award Number:  8414 ;  Skogssällskapet, Grant/ Award Number :  2018-485-Steg 2 2017 ;  Office of Biological and Environmental Research ;  Natural Sciences and Engineering Research Council ;  Svenska Forskningsrådet Formas , Grant/Award Number:  2016-01289  and  942-2015-49 ;  Vetenskapsrådet, Grant/Award Number :  2017-05268 ;  Norges Forskningsråd , Grant/Award Number: 274711;  NASA, Grant/Award Number :  NNH17ZDA001N ,  NNX15AT81A  and  NNX17AE13G ;  Danmarks Grundforskningsfond , Grant/ Award Number: CENPERM DNRF100; Nordic Center of Excellence; Arctic Data Center; EU  FP7-ENV , Grant/Award Number: 282700; Natural Sciences and  Engineering Research Council Discovery Grants; Suomen Akatemia , Grant/Award Number:  286950 ,  312912 ,  314630 ,  317054 ,  325680 ,  332196 ,  337549 ,  33761  and  337552 ;  Greenland Research Council , Grant/Award Number: 80.
DATA AVA I L A B I L I T Y S TAT E M E N T Data are archived and freely available at Zenodo. The synthesis dataset is available at http://doi. org/10. 5281/zenodo. 4519583. Annual and averaged flux predictions are available at http://doi. org/10. 5281/ zenodo. 4521852. The codes to run the statistical models and predictions together with the uncertainty estimation can be found in an R.
F Observed and predicted annual fluxes of GPP (gross primary production; a and d), ER (ecosystem respiration; b and e), and NEE (net ecosystem exchange; c and f) based on model fit (a-c) and predictive performance (d-e). Model fit was evaluated by predicting fluxes over the entire model training data, while predictive performance was assessed using a leave-one-site-out cross validation scheme in which each site was iteratively left out from the dataset, and the remaining data were used to predict fluxes for the excluded site. Model fit and predictive performance statistics (r = Pearson's correlation between observed and predicted fluxes, g; Bias = mean absolute bias, h; RMSE = root mean square error, i across annual fluxes and five modeling methods; GLM = generalized linear model; GAM = generalized additive model; GBM = generalized boosted regression tree; RF = random forest; SVM = support vector machine) and their median ensemble (ENS) are shown in subfigures g-i.
A cement paste was formulated only with the CEM III/C cement, which have the highest GGBFS content of CEM III type cements according to EN 197-1. Pure pastes of GGBFS were made with a mix of several productions of a same production site, water and 5% w/w of NaOH as activator of the hydraulic setting. Cements and GGBFS major composition was established by X-ray Fluorescence (XRF) with a Bruker S4 Explorer spectrometer, on fused beads. Data were recovered and processed with the SpectraPlus software (Table  1 ). GGBFS has usually noticeable amounts of sulfur, usually between 0. 2 and 3. 5 wt. %, mostly under reduced form  (Glasser et al. , 1988; Piatak et al. , 2015). Thus, sulfides are quantified in GGBFS-containing cements and pure GGBFS powders, accordingly to the French cement standard  (NF EN 196-2, 2013)  through an acid distillation and subsequent recovery of H 2 S in an alkaline solution.
Acknowledgments The authors are thankful to the  SARM laboratory (CRPG, Vandoeuvre-lès-Nancy, France)  for its contribution to the element analysis, and to the  LCPME laboratory (Villers-lès-Nancy, France)  for its contribution to the leachates analysis of sulfur anions. The authors are also particularly grateful for the funding of the study from the  ATILH professional association (Paris-La-Defense, France)  under reference  PRA/12-03 , and especially  Horacio Colina  for offering constant support throughout the project. Finally, the authors use vector parts designed by macrovector (from  Freepik. com ) for the graphical abstract and acknowledge them for they work.
01 Hz to 0. 1 Hz. First reports on MAWs date back to the 1960s when  Cook (1969)  observed these waves in North America. According to  Campbell and Young (1963) , auroral activity was known to produce sound in this frequency range (see also  Wilson et al. , 2010) , but  Cook (1969)  found, as a result of triangulation, that his observations traced back to mountainous regions  (Larson et al. , 1971). Therefore, these acoustic waves have been referred to as mountain-associated sound  (Chimonas, 1977)  or, more commonly, as MAWs  (Larson et al. , 1971; Rockway et al. , 1974; Thomas et al. , 1974; Greene and Howard, 1975; Bedard, 1978). Larson et al. (1971)  used data of three sites in the USA -in Alaska, Colorado, and Idaho -and measured amplitudes of 0. 05 Pa to 0. 7 Pa. They considered local noise to be the reason for the daily variation that they found in the number of detections.
Dataset When fully established, the IMS network will consist of 60 infrasound arrays (see map in Fig. 1 ). Differential pressure has been continuously recorded at the IMS infrasound stations for up to 20 years, at a sampling rate of 20 Hz. The detection of infrasound events from these waveform data is performed using the array processing algorithm PMCC  (Cansi, 1995). For this study, filters were applied to the PMCC detection lists according to Table  1 , to focus on significant detections in the frequency range of MAWs. Note that the upper-frequency limit was set at 0. 07 Hz -instead of 0. 1 Hz -to ensure clear discrimination from microbarom detections (0. 1-0. 5 Hz), a persistent infrasound signal originating from interacting ocean waves (e. g. ,  Donn and Rind, 1972; Hupe et al. , 2019).
Source conditions The most dominant MAW source regions -the Southern Alps of New Zealand, the southern Andes, and the Tibetan Plateau -are characterized by strong tropospheric winds all around the year. Therefore, the monthly mean wind fields are not appropriate to analyze the source conditions during MAW events. Instead, the threehourly dataset of the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2, Bosilovich et al. , 2016) was used. The focus is on IS02 for the southern Andes and IS36 for New Zealand. These stations are nearest to the respective source regions, so propagation effects are minimized, and damping of the MAW amplitudes is smallest. The traveling times of the MAWs are shorter than the MERRA-2 time interval; for IS02, the average time is around 36 min (at distance r = 749 km and v eff = 339 m s -1 ), and for IS36, this is around 51 min (at r = 1, 080 km and v eff = 350 m s -1 ), for stratospheric propagation  (Hupe, 2018).
Comparison of the MAW hotspots with satellite-based GW hotspots The question of whether a common source generation mechanism exists for MAWs and OGWs is assessed by comparing global GW hotspot maps with the identified source regions of MAWs. The global GW activity was obtained from the global GW climatology based on atmospheric infrared limb emissions observed by satellite (GRACILE), which was produced by  Ern et al. (2017). GRACILE provides a climatology of GW parameters such as temperature variances, GW potential energy (GWPE), and absolute GW momentum flux (GWMF) in the middle atmosphere. Here, the GWMF data product from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument was used to estimate the global GW activity. SABER products are based on the period from February 2002 to January 2015 (13 years), and thus similar to the infrasound data set.
The MAW source regions were compared with the GWMF at 30 km, the lowest available level. More precisely, the GWMF deviation from the zonal mean was calculated so that positive deviations indicate enhanced GW activity. Lightning data were also taken into account to separate convectively-induced GWs from other sources like topography. Cecil (2015)  produced the HRES monthly climatology of lightning activity. It provides mean flash rates per square kilometer and day in the middle of a month  (Cecil et al. , 2014)  and was composed of data from the Optical Transient Detector and the Lightning Imaging Sensor. In Fig. 10 , color-coded lightning activity and GWMF are shown for January, April, July, and October. The black contour lines reflect the MAW source regions  MAW contour lines equal the threshold of 0.
As was discussed in Section 3, New Zealand's South Island is also a regular source region for MAWs although it does not appear in Fig. 10. Hoffmann et al. (2016) , using satellite observations, identified New Zealand as one of the active source regions of OGWs in the Southern Hemisphere. They evaluated upstream and downstream variances in temperature perturbations at about 40 km altitude, based on 10 years of HRES satellite observations. GWMF is not enhanced over New Zealand in the GRACILE dataset. One reason is the coarse horizontal resolution of the GRACILE climatology -GW parameters were evaluated in bins of 30°× 20°  (Ern et al. , 2018). A second reason is the characteristic wind speed profile above mountain ranges at mid-latitudes. The atmospheric feature was pointed out by  Kruse et al.
In 2016, for example, a mean zonal wind maximum of 32 m s -1 was at 10 km and a local minimum of 14 m s -1 at 19 km in January (Figure  S3  in the Supplements). The critical level was at 15 km in July 2016. Zeng et al. (2017)  reported evidence of OGWs above the Tibetan Plateau. They evaluated nine years of satellite data from the lower stratosphere (15-30 km) and found OGWs during winter and spring. Moreover,  Alexander et al. (2008)  found that enhanced GWPE up to the tropopause was generally filtered at levels of low wind speed below 30 km altitude. The MAW hotspot of the coastal mountain ranges in North America agrees with enhanced GWMF in January. In November, the GWMF deviation is also positive in this region; whereas it equals the zonal mean in At high latitudes in general, the distribution of IMS infrasound arrays compared to the source regions is relatively coarse which prevents for obtaining enough cross-bearing results for events like MAWs.
Summary and conclusions In this paper, a rarely investigated infrasound phenomenon -the MAW -was studied, and global source regions were identified using infrasound measurements of the IMS network. The dataset that covers more than 15 years was processed with the PMCC algorithm, and a cross-bearing method was applied to the monthlyaveraged low-frequency detections between 0. 02 Hz to 0. 05 Hz. A comprehensive analysis of the global hotspots towards both meteorological source and propagation conditions was carried out. The newly identified hotspot in central Asia appears to be the strongest one worldwide. In addition, the southern Andes and the Southern Alps of New Zealand are noticeable source regions of MAW since these are also OGW hotspots in the Southern Hemisphere. At high latitudes, the station distribution is relatively coarse, compared to lower latitudes.
Acknowledgments The authors thank the editor  Alexander Gohm  and the two anonymous reviewers who provided useful comments for improving this manuscript. The research leading to these results has been performed within ARISE2 (http://ARISE-project. eu)that received funding from the  European Commission 's  Horizon 2020 program  under grant agreement  653980. The operational ECMWF HRES atmospheric model analysis defined by the  IFS  is available at https://www. ecmwf. int/en/forecasts/datasets(last access:  28 February 2019 ). MERRA-2 data can be accessed through the  NASA Goddard Earth Sciences Data Information Services Center  via https://gmao. gsfc. nasa. gov/reanalysis/MERRA-2/data_access(last access:  06 May 2019 ). The  International Data Centre in Vienna  is acknowledged for providing the IMS infrasound data.
The age-model allows us to establish modelled ages for the tephra layers within the succession that are not directly dated. The resulting dated tephra record clearly reveals a highly time resolved and previously unparalelled chronicle of explosive activity from the Vulsini, Vico, Sabatini, Colli Albani and Roccamonfina volcanic complexes. Our study provides a benchmark and valuable geochemical and geochronological dataset to be used as a reference for any future development and application of the tephrostratigraphic methods across the central Mediterranean area both during the investigated 430-365 kyr time interval, and deeper in time. This contribution underlines the importance of integrating proximal and distal sedimentary records to more accurately establish long-term and comprehensive volcanic eruption records.
2 ;  Giaccio et al. , 2019). In this paper, we focus on the lowermost ~17-meter-thick interval (between ~80 and ~98 m composite depth) of the F4-F5 core, spanning a ~60 kyr time interval. The interval includes the lowermost tephra layer labelled TF-126, which is directly dated by 40 Ar/ 39 Ar method at 424. 3 ± 3. 2 ka  (Giaccio et al. , 2019) , and up to TF-85, correlated to the Villa Senni eruption (i. e. , Tufo Lionato) from the Colli Albani volcano  (Giaccio et al. , 2019) , which is dated at 367. 6 ± 1. 6 ka  (Marra et al. , 2009; Giaccio et al. , 2012b; Fig. 2). This interval contains 32 ash layers, out of which 28 are investigated and presented in this study, while 4 (i. e. ,  were already studied and reported in  Giaccio et al. (2019;  Table  1 ). In order to improve the reference geochemical dataset required for establishing reliable tephra correlations of the Fucino tephra, we re-examined the key pyroclastic succession at Tuscania-San Pietro (southern Vulsini;  Cioni et al.
Finally, the Plinian fall horizon TSP-4 is attributed to Vico Plinian activity, although with an uncertain correlation. The TSP succession also includes the Casale delle Piane and Castel Broco major pyroclastic flow deposits (investigated also in the present study, see below) and is topped by the Tuscania lavas  (Palladino et al. , 1994 (Palladino et al. , , 2010)) , which are broadly coeval with the Quarticciolo lavas  (356 ± 15 ka; Funicello et al. , 2012)  outcropping in other nearby localities. To supplement the major element datasets for Vico volcano presented in  Pereira et al. , (2020)  With the same aim, we also analysed the Riano R-1 tephra (Fig. 4 ), found within a lacustrine (diatomite) succession outcropping along the Flaminia road (42° 05' 24. 06" N, 012° 31' 57. 43'' E), north of Rome (Fig.
Neverthless, also in those cases, the relatively homogeneity and/or geochemical consistency of the analysed glasses indicate a satisfactory reliability and representativity of the compositional data for tephrochronological purposes. A synthesis of all acquired data for each of the 28 tephra and 4 proximal pyroclastic units can be found in Table  1 , along with information relative to previously investigated tephra from F4-F5 core and proximal volcanics. Polishing and carbon coating of the epoxy slides were performed for electronprobe microanalyser wavelength dispersive spectroscopy (EPMA-WDS) analysis at the Istituto di Geologia Ambientale e Geoingegneria of the Italian National Research Council (IGAG-CNR, Rome). Major and minor elements quantitative analyses were performed with a CAMECA SX-50 EPMA equipped with five-wavelength dispersive spectrometers (WDS), operating at 15 kV accelerating voltage, 15 nA beam current, 10 µm defocused beam diameter -to limit Na mobilization and loss -and 20 s element counting time for all elements.
Wollastonite (Si and Ca), corundum (Al), periclase (Mg), magnetite (Fe), rutile (Ti), orthoclase (K), jadeite (Na), phlogopite (F), potassium chloride (Cl), barite (S), F-apatite (P) and metals (Mn) were used as calibration standards. The Kakanui augite and Rhyolite RLS132 glasses from the United States Geological Survey were measured prior to each analytical run to evaluate the accuracy of the electron-microprobe analysis. Obtained mean values are shown in Supplementary Dataset-4 (SD-4), along with the % difference between each oxide with respect to the recommended values. The mean values show ~0. 5% difference for SiO2, ranging in concentrations from 50 wt% to 77 wt%, <0. 2% for oxides in the 15-16 wt% concentration range, 3. 3-1. 8% for oxide concentrations between 5 and 12 wt%, 4. 2-0.
We adopted 93 wt% as a threshold for the measured total: analysis with total values lower than 93 wt% were discarded. All compositional data are shown as oxide weight percentages (wt%) in the Total Alkali vs Silica (TAS; Le  Maitre et al. , 2002)  classification diagram -as well as bi-plots diagrams -with total iron (FeOt) expressed as FeO and normalized to 100% on a volatile-free basis (i. e. , excluding Cl, SO3 and F volatiles) for correlation purposes. Collected data are all reported in Supplementary Dataset-1 (SD-1), along with secondary standards and MPI-DING measured values.
In addition, MPI-DING reference glasses were used to monitor analytical accuracy  (Jochum et al. , 2006). LA-ICP-MS data reduction was performed in Microsoft Excel, as outlined in  Tomlinson et al. (2010). Accuracies of LA-ICP-MS analyses of the MPI-DING reference glasses, ATHO-G and StHs6/80-G, were typically ≤ 5% for most elements measured. These measurements are provided in Supplementary Dataset-2 (SD-2), along with those of selected proximal eruption units and the full Fucino dataset.
2σmean, i. e. , the standard error with N = 150, was better than ± 0. 000008 for all Sr measurements. Measured 87 Sr/ 86 Sr ratios were normalized for within-run isotopic fractionation to 88 Sr/ 86 Sr = 8. 37521. During the period of isotopic data collection, replicate analyses of NIST-SRM 987 (SrCO3) international reference standard were carried out to check for external reproducibility at 2σ level (where σ is the standard deviation of the standard results, according to  Goldstein et al. , 2003). No correction has been applied to the measured 87 Sr/ 86 Sr values, since the mean measured value of 87 Sr/ 86 Sr for NIST-SRM 987 standard was 0. 710248 ± 0. 000006 (2σ, N = 17), which is indistinguishable from the recommended value of 87 Sr/ 86 Sr = 0. 710248  (Thirlwall, 1991).
Data summary The thickness and main lithological features of the 32 Fucino tephra and proximal volcanic units are summarized in Tables  2  and 3 , respectively. Full glass compositions are provided in Supplementary Dataset-1 (SD-1), while their classification according to the total alkali vs silica (TAS) diagram  (Le Maitre et al. , 2002)  is shown in Figure  6. Description of the geochemistry (i. e. , major, minor and trace elements and Sr-isotopes) of the volcanic glasses is given in the following sections.