dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 1. 1  [19]  with modifications to incorporate error ellipse data  [20]. Prior to model fitting, the raw Argos locations were passed through a speed filter (R package 'argosfilter' v. 0. 63) with a threshold of 8 m s 21 to remove outliers. Analyses of the tracks were restricted to observations made between one week prior to the start of the sonar exposure and one week after its end. One model was fitted per whale with predictions of whale location (with uncertainty) made every 1 h. (iii) DTAG audio recordings DTAG audio files were inspected aurally and visually using spectrograms to identify the start and stop times of foraging sounds produced by the tagged whale, and those produced by other whales. Foraging sounds, consisting of echolocation search clicks and buzzes (which are likely to represent preycapture attempts), were ascribed to the tagged whale depending upon the sounds' relative amplitude and spectral characteristics.
(ii) Mahalanobis distance-based response intensity analysis To investigate effects of source distance and SPLmax (the maximum SPL of the experiments) on a response intensity index, we calculated the MD between the baseline-period average and the averages of 35 min windows (the longest exposure duration) without overlap for the set of movement variables. Due to the limited number of experiments (n ¼ 3), this analysis also included DTAG data from the published 2013 experiment (n ¼ 1 exposed whale, intermediate source distance; electronic supplementary material, table  S2 ) and from baseline tags (n ¼ 10 whales) with representative natural behaviour collected near Jan Mayen in June 2013 -2016  [13]  (electronic supplementary material, figure  S4 ). These MDs indicated how much each whale's movement behaviour within a time-bin deviated from the average baseline behaviour of all whales.
(iii) State-based modelling for satellite tags To investigate potential avoidance responses by the satellitetagged whales, we fitted hidden Markov models using R package 'MomentuHMM' v. 1. 4. 2 [25]  to the posterior mean locations of all filtered tracks. Models were built using hourly step lengths and turn angles, using G and von Mises distributions, respectively. Observations for time-steps without raw ARGOS locations were treated as missing data. The fitted models separated the data into tortuous movements (state 1), lower-speed directional movements (state 2) and high-speed directional movements (state 3). Models with three states were selected over models with two states based upon AIC and biological realism; models with four states did not converge. Based on the expectation that avoidance responses would involve switching out of the non-directional state, we modelled probabilities of transitioning from state 1 to each of the three states separately as function of covariates  [26].
Two other tags were deployed 6 h apart (electronic supplementary material, table  S1 ) on individuals that were not visually confirmed to be in the same group, but their horizontal tracks (electronic supplementary material, figures S6 and S7) and dive profiles were also sometimes correlated. Therefore, we checked whether the results for all satellite-tagged whales were robust against non-independence of individuals by repeating the model selection procedure after omitting different combinations of one or two individuals from the dataset.
(iv) Analysis of click-absent periods for animals near the mooring location The analysis of the PAM recordings aimed at detecting cessations of sound production, which is a common response of beaked whales to sonar  [13]. Whale groups were considered sufficiently close to the recorder to determine whether a response occurred if at least one click-present period coincided with the last hour of the pre-exposure. If that was the case, we compared the duration of the last click-absent period that started during sonar exposure to the empirical cumulative distribution of the durations of clickabsent periods observed during a control period specified as data recorded the same year prior to the first sonar experiment. Durations of click-absent periods were calculated from the output of the detector, although click-absent periods that started during a sonar experiment were also manually audited for the presence of fainter clicks that could be missed by the detector.
There was more support for a sonar effect that only depended upon SPLmax than for one that only depended upon source distance (DAIC 10. 6). These results were relatively robust against reduction in the dataset to account for potential non-independence of individuals (electronic supplementary material, table  S5 ). However, ID161593 was particularly influential, and exclusion of this tag together with ID161588 reduced DAICs to less than 2. Predictions from the selected model indicated that the satellite-tagged whales' movements were less likely to remain tortuous and more likely to transition from tortuous to highspeed directional at the time of the exposure, compared to baseline (electronic supplementary material, figure  S5 ).
Acknowledgements. We would like to thank everyone involved in the Jan Mayen fieldwork, including  Captain Christian Harboe-Hansen  and the rest of the ship's crew and science crew,  Miguel Neves dos Reis ,  Eilidh Siegal ,  Eva Hartvig ,  Mike Williamson ,  Naomi Boon ,  Joanna Kershaw ,  Hannah Wood ,  Dave Moretti  and  Ron Morrissey. Invaluable  scientific advice was provided by  Sacha Hooker ,  Nicoletta Biassoni ,  Benjamin Benti ,  Marije Siemensma ,  Filipa Samarra ,  Michael Ainslie ,  Mark Prior , the editor and three anonymous reviewers.
Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere -the "global carbon budget" -is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO 2 emissions (E FF ) are based on energy statistics and cement production data, while emissions from land use change (E LUC ), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO 2 concentration is measured directly and its growth rate (G ATM ) is computed from the annual changes in concentration.
The ocean CO 2 sink (S OCEAN ) and terrestrial CO 2 sink (S LAND ) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (B IM ), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009-2018), E FF was 9. 5±0. 5 GtC yr -1 , E LUC 1. 5±0. 7 GtC yr -1 , G ATM 4. 9±0. 02 GtC yr -1 (2. 3±0. 01 ppm yr -1 ), S OCEAN 2. 5±0. 6 GtC yr -1 , and S LAND 3. 2±0. 6 GtC yr -1 , with a budget imbalance B IM of 0. 4 GtC yr -1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in E FF was about 2.
Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959-2018, but discrepancies of up to 1 GtC yr -1 persist for the representation of semi-decadal variability in CO 2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO 2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO 2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al.
542 ppm, calculated here from overlapping data during 1980-2012. of atmospheric CO 2 measurements), the last decade  (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) , and the current year  (2019). We quantify the input of CO 2 to the atmosphere by emissions from human activities, the growth rate of atmospheric CO 2 concentration, and the resulting changes in the storage of carbon in the land and ocean reservoirs in response to increasing atmospheric CO 2 levels, climate change and variability, and other anthropogenic and natural changes (Fig. 2 ). An understanding of this perturbation budget over time and the underlying variability and trends in the natural carbon cycle is necessary to also understand the response of natural sinks to changes in climate, CO 2 and land use change drivers, and the permissible emissions for a given climate stabilisation target.
The CO 2 sinks as defined here conceptually include the response of the land (including inland waters and estuaries) and ocean (including coasts and territorial sea) to elevated CO 2 and changes in climate, rivers, and other environmental conditions, although in practice not all processes are fully accounted for (see  Sect. 2. 7). The global emissions and their partitioning among the atmosphere, ocean, and land are in reality in balance; however due to imperfect spatial and/or temporal data coverage, errors in each estimate, and smaller terms not included in our budget estimate (discussed in Sect. 2. 7), their sum does not necessarily add up to zero. We estimate a budget imbalance (B IM ), which is a measure of the mismatch between the estimated emissions and the estimated changes in the atmosphere, land, and ocean, with the full global carbon budget as follows: E FF + E LUC = G ATM + S OCEAN + S LAND + B IM.
Methods Multiple organisations and research groups around the world generated the original measurements and data used to complete the global carbon budget. The effort presented here is thus mainly one of synthesis, where results from individual groups are collated, analysed, and evaluated for consistency. We facilitate access to original data with the understanding that primary data sets will be referenced in future work (see Table  2  for how to cite the data sets). Descriptions of the measurements, models, and methodologies follow below and detailed descriptions of each component are provided elsewhere. This is the 14th version of the global carbon budget and the eighth revised version in the format of a living data update in Earth System Science Data. It builds on the latest published global carbon budget of Le  Quéré et al.
BP's data set explicitly covers about 70 countries (96 % of global energy emissions), and for the remaining countries we use growth rates from the subregion the country belongs to. For the most recent years, natural gas flaring is assumed constant from the most recent available year of data (2017 for Annex I countries, 2016 for the remainder). Cement. Estimates of emissions from cement production are taken directly from  Andrew (2019). Additional calcination and carbonation processes are not included explicitly here, except in national inventories provided by Annex I countries, but are discussed in Sect. 2. 7. 2. Country mappings. The published CDIAC data set includes 257 countries and regions. This list includes countries that no longer exist, such as the USSR and Yugoslavia. We reduce the list to 214 countries by reallocating emissions to currently defined territories, using mass-preserving aggregation or disaggregation.
, 2011b). The consumption-based emissions have the same global total, but they reflect the trade-driven movement of emissions across the Earth's surface in response to human activities. We estimate consumption-based emissions from 1990 to 2016 by enumerating the global supply chain using a global model of the economic relationships between economic sectors within and between every country  (Andrew and Peters, 2013; Peters et al. , 2011a). Our analysis is based on the economic and trade data from the Global Trade Analysis Project (GTAP;  Narayanan et al. , 2015) , and we make detailed estimates for the years 1997  (GTAP version 5), 2001 (GTAP6), 2004 , 2007 , and 2011 (GTAP9. 2) , covering 57 sectors and 141 countries and regions. The detailed results are then extended into an annual time series from 1990 to the latest year of the gross domestic product (GDP) data (2016 in this budget), using GDP data by expenditure in the current exchange rate of US dollars (USD; from the UN National Accounts Main Aggregates Database; UN, 2017) and time series of trade data from GTAP (based on the methodology in  Peters et al.
, 2011b). We estimate the sector-level CO 2 emissions using the GTAP data and methodology, include flaring and cement emissions from CDIAC, and then scale the national totals (excluding bunker fuels) to match the emission estimates from the carbon budget. We do not provide a separate uncertainty estimate for the consumption-based emissions, but based on model comparisons and sensitivity analysis, they are unlikely to be significantly different than for the territorial emission estimates  (Peters et al. , 2012a).
In contrast, the BLUE model includes sub-grid-scale transitions at the grid level between all vegetation types as indicated by the harmonised land use change data (LUH2) data set (https://doi. org/10. 22033/ESGF/input4MIPs. 1127;  Hurtt et al. , 2011 Hurtt et al. , , 2019)). Furthermore, H&N2017 assume conversion of natural grasslands to pasture, while BLUE allocates pasture proportionally on all natural vegetation that exists in a grid cell. This is one reason for generally higher emissions in BLUE. For both H&N2017 and BLUE, we add carbon emissions from peat burning based on the Global Fire Emission Database (GFED4s;  van der Werf et al. , 2017)  and peat drainage based on estimates by  Hooijer et al. (2010)  to the output of their bookkeeping model for the countries of Indonesia and Malaysia.
(2018). h Compared to published version: revised parameter values for photosynthetic capacity for boreal forests (following assimilation of FLUXNET data), updated parameter values for stem allocation, maintenance respiration and biomass export for tropical forests (based on literature), and CO 2 down-regulation process added to photosynthesis. Hydrology model updated to a multi-layer scheme (11 layers). i See also  Woodward and Lomas (2004). j See also  Ito and Inatomi (2012). k See also  Remaud et al. (2018). cropland, pasture, and rangeland. The LUH2 data provide a distinction between rangelands and pasture, based on inputs from HYDE. To constrain the models' interpretation on whether rangeland implies the original natural vegetation to be transformed to grassland or not (e. g. browsing on shrubland), a forest mask was provided with LUH2; forest is assumed to be transformed, while all other natural vegetation remains.
org/10. 22033/ESGF/input4MIPs. 1127;  Hurtt et al. , 2011 Hurtt et al. , , 2019 ; Table  A1 ). This new data set is of quarterdegree fractional areas of land use states and all transitions between those states, including a new wood harvest reconstruction, new representation of shifting cultivation, crop rotations, and management information including irrigation and fertiliser application. The land use states include five different crop types in addition to the pasture-rangeland split discussed before. Wood harvest patterns are constrained with Landsat-based tree cover loss data  (Hansen et al. , 2013). Updates of LUH2 over last year's version use the most recent HYDE-FAO release (covering the time period up to and in-cluding 2015), which also corrects an error in the version used for the 2018 budget in Brazil.
GOBM evaluation and uncertainty assessment for S OCEAN The mean ocean CO 2 sink for all GOBMs falls within 90 % confidence of the observed range, or 1. 6 to 2. 8 GtC yr -1 for the 1990s. Here we have adjusted the confidence interval to the IPCC confidence interval of 90 % to avoid rejecting models that may be outliers but are still plausible. The GOBMs and flux products have been further evaluated using air-sea CO 2 flux (f CO 2 ) from the SOCAT v2019 database  (Bakker et al. , 2016, updated). We focused this evaluation on the root-mean-square error (RMSE) between observed f CO 2 and modelled pCO 2 and on a measure of the amplitude of the interannual variability of the flux  (Rödenbeck et al. , 2015). The amplitude of the S OCEAN interannual variability (A-IAV) is calculated as the temporal standard deviation of a 12-month running mean over the CO 2 flux time series  (Rödenbeck et al.
ILAMB variables cover key processes that are relevant for the quantification of S LAND and resulting aggregated outcomes. The selected variables are vegetation biomass, gross primary productivity, leaf area index, net ecosystem exchange, ecosystem respiration, evapotranspiration, soil carbon, and runoff (see Fig. B2  for the results and for the list of observed databases). Results are shown in Fig. B2  and discussed in Sect. 3. 1. 3. For the uncertainty for S LAND , we use the standard deviation of the annual CO 2 sink across the DGVMs, averaging to about ±0. 6 GtC yr -1 for the period from 1959 to 2018. We attach a medium confidence level to the annual land CO 2 sink and its uncertainty because the estimates from the residual budget and averaged DGVMs match well within their respective uncertainties (Table  5 ).
Model evaluation The evaluation of the ocean estimates (Fig. B1 ) shows a RMSE of 15 to 17 μatm for the three pCO 2 -based flux products over the globe, relative to the pCO 2 observations from the SOCAT v2019 database for the period 1985-2018. The GOBM RMSEs are a factor of 2 to 3 larger and range between 29 and 49 μatm. The RMSEs are generally larger at high latitudes compared to the tropics, for both the flux prod-ucts and the GOBMs. The three flux products have similar RMSEs of around 12 to 14 μatm in the tropics, around 17 to 18 μatm in the north, and 17 to 24 μatm in the south. Note that the flux products are based on the SOCAT v2019 database; hence these are no independent data sets for the evaluation of the flux products. The GOBM RMSEs are more spread across regions, ranging from 21 to 34 μatm in the tropics, 32 to 48 μatm in the north, and 31 to 77 μatm in the south.
Data availability The data presented here are made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights into how the carbon cycle works, how humans are altering it, and how we can mitigate the resulting human-driven climate change. The free availability of these data does not constitute permission for publication of the data. For research projects, if the data are essential to the work, or if an important result or conclusion depends on the data, co-authorship may need to be considered for the relevant data providers. Full contact details and information on how to cite the data shown here are given at the top of each page in the accompanying database and summarised in Table 2. Table  A3. Comparison of the inversion setup and input fields for the atmospheric inversions.
Acknowledgements. We thank all people and institutions who provided the data used in this carbon budget as well as  Vivek Arora ,  Jinfeng Chang ,  Eunkyoung Choi ,  Julie Deshayes ,  Christian Ethé ,  Matthew Fortier ,  Tristan Quaife ,  Shijie Shu ,  Anthony Walker , and  Ulrich Weber  for their involvement in the development, use and analysis of the models and data products used here. We thank  Ed Dlugokencky  for providing atmospheric CO 2 measurements. We.
The occurrence of faecal pellets, increases in grain size, TOC, and CaCO 3 observed in the HVMIs studied here points towards enhanced productivity, which is likely to be a regional phenomenon. If this was not limited to the Cleveland Basin, it may explain a coeval organic enrichment across the Laurasian Seaway. The causal mechanism behind the increase in productivity cannot be constrained with the present dataset. However, slight increases in depositional energy may result from intermittent storm mixing or changes in bottom water currents, which could both increase primary productivity. Armstrong et al. (2016)  attributed alternations in TOC enrichment to orbitally forced changes in humidity-aridity occurring over a short eccentricity timescale (100 kyr). Based on the chronostratigraphic time frame for the Yorkshire and Dorset sections  (Armstrong et al.
, 2016; Huang et al. , 2010)  and assuming a linear sedimentation rate, the changes between the HVMIs and LVMIs equate with short eccentricity cycles. Thus, our observations match the prediction of Armstrong et al. (  2016 ) that enhanced TOC burial occurred during wet periods through enhanced continental weathering, nutrient supply to the ocean, and primary productivity. The astrochronological timescale established for the Swanworth Quarry 1 Core in the Wessex Basin  (Huang et al. , 2010)  and applied to the Ebberston 87 Core in the Cleveland Basin  (Armstrong et al. , 2016)  is plotted for reference in Fig. 1a. Given the relatively short duration of the studied interval, it is not possible to conduct further spectral analysis with the present dataset.
Conclusions and conceptual model This study provides insight into the sedimentation in an epicontinental seaway during a greenhouse world. Examining sediment away from strong detrital dilution and overprint allows us to unpick the global controls on sedimentation and gives us further insight into the key processes in this palaeogeographic setting. Far away from detrital inputs, this location provides an excellent dataset for examining changes in water column processes. The studied interval of the Kimmeridge Clay Formation in the Cleveland Basin was deposited in a highly dynamic environment that fluctuated from LVMI deposition to HVMIs (Fig. 11 ). During LVMI sedimentation (Fig. 11a ), the wawww. clim-past. net/15/1581/2019/ Clim. Past, 15, 1581-1601, 2019 ter column was oxic and was able to sustain life to a degree that the sediment was extensively bioturbated.
Data availability. Data is available in the PANGAEA data repository via: https://doi. org/10. 1594/PANGAEA. 903744 (Atar et al. , 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-15-1581-2019-supplement. Author contributions. EA, CM, AA and TW were responsible for conceptualization, formal analysis, investigation, and methodology. In addition, CM, AA and TW also provided project supervision. OD, LH, VLV, ML and BS were responsible for investigation and methodology. All authors contributed to the writing and revision of this manuscript.
Data Collection and Analysis The data presented in this letter were collected over the course of the first two encounters of the Sun by Parker Solar Probe in November 2018 and April 2019. This study makes use of all of the in situ instruments on the spacecraft. Thermal plasma properties are measured by the PSP SWEAP instrument suite  10  , including the Solar Probe (SPC) Cup, SPAN electron, and SPAN ion plasma data. Magnetic field data from the outboard FIELDS magnetometer was also used  11, 15  , along with energetic particle rates as seen by IS IS  18. SPC measures the reduced distribution function of ionized hydrogen and helium and the two dimensional flow angles of the ions as a function of energy/charge. These measurements are performed at least once per second and typically more than four times per second throughout the encounter phase of each orbit (below 0.