dataesr/scientific-paragraphs-categorization huggingface dataset:
Vaults; and Pacific Marine Environmental Laboratory-Autonomous Underwater Hydrophones (PMEL-AUH). Different instruments may have different capabilities, including depth rating, system frequency response, and duty cycle requirements, and these further affect the performance of an automated detector  54. For example, Table  1. Previous analyses of long-term datasets that have used automated algorithms to detect calls and report spatial distribution and/or temporal occupancy of Antarctic blue whales. a False positive rate reported only for months when there were more than 500 calls detected. b False positive detections were from a different detector operating in an adjacent frequency band with a similar, frequency-adjusted, spectrogram correlation kernel. c True positive rates for this detector for high, medium, and low signal to noise ratio (SNR) calls and a variety interfering noises reported by Socheleau et al.
2015, but the prevalence of these conditions within the full dataset is not indicated. d False negative rate reported as a percentage of total uncorrected detections for a 20% subset of days without automated detections. Subsampling from each dataset. Moorings in the Antarctic are typically recovered and serviced at the most once a year due to their remote locations, potentially long periods of ice cover, and reduced/negligible access during Antarctic winter. Thus we define a site-year as a recording from a single instrument and site that is approximately a year in duration. A subset of approximately 200 h of data was selected from each site-year for annotation. This number of hours was chosen a priori and was constrained by budgetary limits, but it was believed to be a reasonable trade-off among analyst time, maintaining adequate sample sizes within each siteyear, and annotating a sufficient number of different site-years.
For each site-year a systematic random subsampling scheme was used to generate a representative set of acoustic recordings from the larger dataset. The systematic random subsampling scheme consisted of: Table  2. Description of annotated datasets including the site-year, location, initials of the analyst who made the annotations, type of instrument, start and stop date of the annotations, and number of hours (independent dates and times) annotated, as well as the total duration of the recordings annotated. a Not evenly distributed throughout the year. 1. splitting the dataset into "chunks" of time. The optimal length of a time chunk will be species and study specific. For the annotated library time was split into mostly hour-long chunks with some exceptions. 2. calculating the spacing between chunks, t s to ensure that the desired sample size of time chunks is created and that there was broad representation of hours in the day across all chunks.
For the annotated library, spacing was calculated such that there were at least 150, and usually nearer to 200, annotated periods between the first and last available chunks. 3. Picking a random number between 1 and t s , the spacing, to determine the starting chunk (this was the random element of the subsampling scheme). The aim of the subsampling scheme was to capture a representative sample of the signals recorded for a given site-year, i. e. , to select periods of time with calls that spanned a range of signal-to-noise ratios (SNR) and periods of time without calls, as well as other sounds that might contribute to false positives (though rare events may have been missed). Having a temporally representative subsample of sounds was deemed necessary to understand how a detector would perform when used across the entire dataset.
For each site, 10-18 h of data were annotated per month with the exceptions of the Ross Sea in 2014 which had no data for January, and Maud Rise 2014 which recorded only from Jan-Sep (Fig. 2 ). Over the whole year the subsampling scheme ensured a relatively even distribution of hours across a 24 h cycle, with each site containing between 5 and 10 h inspected for any given hour in the cycle. However, four sites had been annotated previously, and thus used slightly different subsampling schemes. Additionally, three of these sites had recording duty cycles shorter than an hour. Elephant Island 2013 and 2014 had a duty cycle of 5 min/h. Maud Rise 2014 had a duty cycle of 25 min/hour. For Elephant Island 2014 9 days per month were selected (randomly, but with roughly even spacing between them), and all 5 min segments were analysed for selected days (total duration of all audio segments 216 h).
Results Distribution of annotations throughout the library. The annotated library consisted of 1880. 25 h (audio duration) of annotated data across 11 site-years and 7 sites. In total, there were 105,161 annotations across all sites, though the numbers of annotations were neither evenly distributed by site nor classification (Table  6 ). Bm-Ant-A was the most numerous annotation with 24,363 manual detections in total, while Bm-Ant-Z was the least numerous annotation with 2,515 manual detections in total. Ross Sea 2014 had the fewest annotations over all site-years with only 359 annotations (104 of Bm-Ant-A, and the remainder unidentified). Elephant Island 2014 had the most annotations of all site-years with 21,438 in total including unidentified sounds. The percentage of hours with each type of annotation was also variable across sites (Table  6 ).
At Elephant Island 2014 Bp-Downsweep had a maximum in January. While there is a temptation to speculate on the drivers of these temporal trends, such analyses are beyond the scope of this work, which was the creation of a dataset suitable for characterising automated detectors. Rather, the purpose of plotting monthly number of annotations by site is simply to describe the contents of the Annotated Library and to identify months or seasons that do and do not have sufficient number of detections to allow characterisation of a detector. In that regard, there is a notable lack of fin whale annotations (Bp-20 Hz, Bp-20Plus, and Bp-Downsweep) from July-December. An example of using the annotated data to examine the performance of automated detectors. ROC, precision-recall, and SNR. ROC and PR curves indicated that detector performance was fair-topoor for these datasets.
The annotations in the library form a representative ground-truth dataset that can be used to extract the features of each call type, and also to train and characterise the performance of automated detectors. Detector performance. To test the utility of the library, we characterised the performance of a spectrogram correlation detector for blue whale calls and an energy sum detector for fin whale calls. The performance of the automated detectors varied by site-year. Neither detector performed particularly well, and some sites and years showed much worse performance than others (Fig. 7 ). Differences in detector performance broadly followed differences in SNR across sites such that sites with lower SNR had worse performance than those with higher SNR (Fig. 8 ). Across sites, the automated detectors showed greater variability at low SNR than at high SNR (Fig.
The library and methods we present here for assessing the performance of the detectors are a step away from estimating call-density, which in turn is a step away from estimating animal density  68. None of the passive acoustic studies of Antarctic blue or fin whales to date (listed in Table  1 ) have completely reported on the performance of their detector over a representative subsample of their data. The methods we have presented here for characterising the performance of a detector on a representative subsample of data constitute a bare minimum of reporting for future studies that utilise automated detectors to study Antarctic blue and fin whale calls. Specifically, reporting should include all parameters for the automated detector including any noise We hope the open-access annotated library we have presented here can provide a base dataset upon which to develop improved detectors i.
e. with higher true positive rates and lower false positive rates. Here we have extracted duration and frequency measurements from annotations, but the library can readily be used to extract more complex features such as pitch-tracks  69  or other time-frequency features  70  to train machine learning algorithms  37, 71, 72  , deep neural networks  73  , or other any other advanced detectors that may provide better performance than the spectrogram correlation detector. Better detectors would not only reduce a source of uncertainty in estimating call-density, but would also reduce the amount of analyst effort required to verify true positives and account for false positives. Future development of this dataset will aim to expand the annotated library to serve as a test-bed for subsequent analyses that address the issues of noise, detection range, and analyst variability to produce standardised outputs that are appropriate for circumpolar comparisons of call-density.
Conclusions We created an annotated library of blue and fin whale sounds that spans four circumpolar Antarctic recording regions, five different years (2005, 2013, 2014, 2015, 2017), and five different types of instrument. The annotations in the library form a representative ground-truth dataset and we demonstrate how to train, test, and characterise the performance of two common automated detectors using the library. The annotated library we present here can serve as a benchmark upon which detectors can be developed, compared, and improved upon. It may also serve as a base dataset to develop additional analytical techniques to enable robust comparisons of acoustic detections of blue and fin whale across diverse circumpolar sites and over long spans of time. We encourage further contributions of data and annotations to help expand the library, and in the future hope to include annotations of sounds from additional Antarctic species, as well as data from other recording locations.
Acknowledgements The annotated library was made possible with funding from:  IWC-SORP  Grant "An annotated library of underwater acoustic recordings for testing and training automated algorithms for detecting Southern Ocean baleen whales";  IWC-SORP  Grant "IWC-SORP Project 5-Acoustic trends in abundance, distribution, and seasonal presence of Antarctic blue whales and fin whales in the Southern Ocean: 5-year strategic meeting";  Australian Antarctic Science Projects 4101 , and 4102-Antarctic baleen whale habitat utilisation and linkages to environmental characteristics, and Population abundance, trend, structure and distribution of the endangered Antarctic blue whale; Research grants from the  Korean Ministry of Oceans and Fisheries  ( KIMST20190361 ;  PM20020 ;  PE20230 ). This report is NOAA/PMEL contribution number  5005.
2017 42 Spectrogram correlation (Ishmael) Not reported 99. 998% d Not reported All ally inspected to determine false negative rate. Visual inspection of all detections to remove false positives Visual inspection of entire Shabangu et al. 2017 Spectrogram correlation (XBAT) Not reported 42-83% Not reported All dataset (1518 h) to assess remove false positives and include missed detections.
Dataset name (site- year) Latitude Longitude Analyst Instrument Start Stop Unique calendar hours with annotations Unique calendar days with annotations Audio duration annotated (h) Maud Rise 2014 65° 0. 00′ S 2° 30. 00′ E BSM AURAL 2014-01-12 2014-09-17 201 201 83. 3 Greenwich 64S 2015 64° 00. 32′ S 0° 00. 22′ E NB Sono. Vault 2015-01-02 2015-12-31 190 190 31. 7 S Kerguelen Plateau 2005 62° 35. 44′ S 81° 15. 64′E NB ARP 2005-01-31 2006-01-31 200 200 200 S Kerguelen Plateau 2014 62° 22. 81′ S 81° 47. 81′ E NB AAD-MAR 2014-02-22 2015-02-20 200 200 200 S Kerguelen Plateau 2015 " " NB AAD-MAR 2015-02-10 2016-01-27 200 200 200 Casey 2014 63° 47. 73′ S 111° 47. 23′ E NB AAD-MAR 2013-12-25 2014-12-12 194 194 194 Casey 2017 " " MA AAD-MAR 2016-12-15 2017-11-07 185 185 185 Ross Sea 2014 75° 01. 19′ S 164° 35.
In addition, Fitzhugh  [60] [61] [62]  thoroughly discussed the philosophical issues in comparing phylogenetic hypotheses generated by different datasets of characters, as made by Stiller et al. [59]  to combine the "backbone" with the main analysis. Fitzhugh also argued against the combination of morphological and molecular data, as well as against molecular phylogenies per se, also due to philosophical issues  [62]. We consider that these major changes still need to be re-evaluated, given that only one species of Melinninae was included and the limited sampling of species of Telothelepodidae and Thelepodidae and the validity of plotting morphological characters onto genetic trees. For those reasons, we prefer to follow herein the classification proposed by Nogueira et al. [1] , which was subsequently confirmed in the phylogenetic analyses of Polycirridae  [46] , and Telothelepodidae  [47].
However, this may change as additional species are added to the dataset after sequencing. Another phylogenetic study on the relationships within Terebellidae s. s. was performed by Jirkov and Leontovich  [63] , which focused on the animals with large lateral lobes only, which they suggest form a monophyletic clade in the family, although the reasons for this were not given. The authors included 93 taxa with large lateral lobes and a single "outgroup" species without lobes, Terebella lapidaria Linnaeus, 1767, the type species of the family. They also considered the presence of short-handled or long-handled anterior uncini as a specific character, rather than generic, as had traditionally been considered. As a result, the authors considered only the following genera with large lateral lobes as valid: Axionice Malmgren, 1866, Lanicides Hessle, 1917, Lanicola Hartmann-Schröder, 1986, Pista Malmgren, 1866 and Scionella Moore, 1963, and synonymised under Axionice the genera Betapista Banse, 1980, Eupistella Chamberlin, 1919, Euscione Chamberlin, 1919, Lanice Malmgren, 1866, Loimia Malmgren, 1866, and Paraxionice  Fauchald, 1972.
Molecular studies on Terebelliformia so far have resulted in 222,406 sequences available for Alvinellidae in Genbank (mostly transcriptomics and phylogenetic markers), 1011 for Ampharetidae, 1298 for Pectinariidae, 2588 for Terebellidae s. l. , and 1476 for Trichobranchidae, considering mitochondrial and nuclear gene markers (Figure  10 ) (Table  3 ). Morphology-based polychaete taxonomy is largely based on external characters, particularly in Terebelliformia. Hessle  [21]  suggested a classification based on the structure of nephridia, however this requires dissection of the specimens, which is not feasible with museum material. However, a technique recently developed, computerized microtomography (µCT), scans the specimens and gives amazing 3D images of their internal anatomy (Figure  9 ), not causing any damage to the specimens, thus allowing type material to be examined.
Funding: O. C. receives a post-doctoral fellowship from FUSP and Shell Brasil; J. M. M. N. receives a productivity grant from CNPq, "Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil, level 2; N. L. has received financial support from the French State in the frame of the "Investments for the future" Programme IdEx Bordeaux, reference ANR-10-IDEX-03-02.
Current estimates of the global land carbon sink contain substantial uncertainties on interannual timescales which contribute to a non-closure in the global carbon budget (GCB) in any given year. This budget imbalance (BIM) partly arises due to the use of imperfect models which are missing or misrepresenting processes. One such omission is the separate treatment of downward direct and diffuse solar radiation on photosynthesis. Here we evaluate and use an improved high-resolution (6-hourly), gridded dataset of surface solar diffuse and direct fluxes, over 1901-2017, constrained by satellite and ground-level observations, to drive two global land models. Results show that tropospheric aerosol-light interactions have the potential for substantial land carbon impacts (up to 0. 4 PgCyr-1 enhanced sink) at decadal timescales, however large uncertainties remain, with models disagreeing on the direction of change in carbon uptake.
Until now, the community has not had a global solar radiation forcing dataset including these aerosol-light effects, to be able to reduce BIM. In this study we first evaluate an improved global forcing dataset of total and diffuse radiation, i. e. improved aerosol scattering, higher temporal resolution, and extended study period to 2017 (see section 2 and supplementary section 1). We then investigate the impact of changes in atmospheric aerosols on the spatio-temporal land carbon sink and assess uncertainties and the implication for BIM using two global land surface models; JULES-ES  [19]  and ORCHIDEE_DF  [20] , employed over the extended period 1900-2017. We restrict our BIM analysis on the period 1959-2017, as widespread observations of atmospheric CO 2 concentrations do no not exist prior to 1959.
DGVM evaluation The degree to which we can accurately quantify aerosol impacts on carbon uptake and BIM depends on the ability of JULES and ORCHIDEE to capture observed patterns of canopy photosynthesis in a variety of light conditions. We therefore evaluate the performance of simulated canopy GPP using halfhourly or hourly values of eddy flux data and meteorology from four forest sites; tropical broad leaved tree Tapajos, Brazil (BRA) (54. 96 • W, 2. 86 • S) and Guyaflux, French Guiana (52. 92 • W, 5. 28 • N), boreal broad leaved tree Saskatchewan, Canada (106. 20 • W, 53. 63 • N), and boreal needle leaved tree Hyytiala, Finland (24. 29 • E, 61. 85 • N) from the FLUXNET201521 dataset. For this evaluation we kept model photosynthetic parameters (e. g. Vcmax) at the global values which could lead to biases in our site level evaluation.
75 and <0. 3 to discriminate between these two cases. Site data is from the FLUXNET2015 dataset  [30]. responses of tropical forest productivity to reductions in total surface radiation. In JULES-ES, the reduced radiation outweighs any benefit from increased diffuse fraction (supplementary figure  10 ), implying that tropical photosynthesis in JULES-ES is lightlimited, perhaps due to the high cloud cover at these latitudes  [23]. ORCHIDEE_DF shows a widespread increase in productivity due to dimming induced soil moisture increases, except in the wettest rainforests of South America and Southeast Asia (SEAS) (supplementary figures 9-11). Overall, for ORCH-IDEE_DF, the dimming induced increase in global carbon uptake is double that of the associated increase in diffuse fraction (supplementary figure  10 ).
Years 2013-2017 are assumed to be background years so replicate the background year 2010. That assumption is supported by the Global Space-based Stratospheric Aerosol Climatology time series (1979-2016)  [29]. The time series of cloud fraction is obtained by scaling the 6-hourly distributions simulated in the Japanese Reanalysis (JRA)  [35]  to match the monthlyaveraged cloud cover in the CRU TS v4. 03 dataset  [36]. Surface radiative fluxes account for aerosolradiation interactions from both tropospheric and stratospheric aerosols, and for aerosol-cloud interactions from tropospheric aerosols, except mineral dust. The radiative effects of aerosol-cloud interactions are assumed to scale with the radiative effects of aerosol-radiation interactions, using regional scaling factors derived from HadGEM2-ES.
Acknowledgments M O S, P F and S S have received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement No. 821003  (project  4C ), and the  Newton Fund  through the  Met Office Science for Service Partnership Brazil (CSSP Brazil ). Surface solar radiation fields were produced by  N B  and can be found at https://figshare. com/ authors/Nicolas_Bellouin/10653083.
Data and code owned by the authors: all the code used for the analysis in this study is archived on Zenodo under the DOI: 10. 5281/zenodo. 5596516 (www. doi. org/10. 5281/zenodo. 5596516). All the 2000-2019 averages from our MAR simulations with and without drifting snow are available via https://zenodo. org/record/5037197 under the DOI: 10. 5281/zenodo. 5037197. Data not owned by the authors: We retrieved the "The Climate Data Guide: Combined CloudSat spaceborne radar and CALIPSO spaceborne lidar cloud fraction dataset" (last modified 21 April 2014) from https://climatedataguide. ucar. edu/climatedata/combined-cloudsat-spaceborne-radar-and-calipso-spaceborne-lidar-cloud-fractiondataset and gratefully acknowledge Jennifer Kay and the National Center for Atmospheric Research Staff (Eds). The ERA5 data is available via the COPERNICUS Climate Data Store (https://cds.
climate. copernicus. eu/#!/home), we use the monthly averaged "total cloud cover" variable which can be accessed via https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthlymeans?tab=form. The weather station data used for comparison with MAR is a compilation of various sources. D17: https://zenodo. org/record/4139737 (DOI: 10. 5281/zenodo. 4139737) Dome C II: freely downloaded from the BSRN website: https://bsrn. awi. de/ and via Pangea (https://doi. pangaea. de/10. 1594/PANGAEA. 935421) (Lupi et al. , 2021). Halley: General meteorological variables (temperature, wind, pressure) can be downloaded via https://doi. org/10. 5285/4E963D9B-DA5D-43D5-A605-FC44EAD63D97 (King et al. , 2021). Additional variables related to the surface energy budget (shortwave and longwave fluxes) can be requested via: src@bas.
ac. uk Amundsen Scott: Via BSRN and Pangea (https://doi. pangaea. de/10. 1594/PANGAEA. 150004) (Dutton & Michalsky, 2015). Neumayer, Sorasen et Panda-1: can be downloaded via Pangea (https://doi. pangaea. de/10. 1594/PANGAEA. 919128) (Schmithüsen, 2020). From AWS4 to AWS19: Via Pangea (https://doi. pangaea. de/10. 1594/PANGAEA. 910473) (Jakobs et al. , 2020).
UV spectrometry The spectrophotometer used was an Agilent Cary 100 dual-beam spectrophotometer equipped with the Cary WinUV data acquisition system. It allows a repetitive scanning of spectra between 180 nm and 900 nm, programmable as a function of time, and measurements of optical density or its derivatives at a given wavelength. Measurements were made with Hellma ® brand Suprasil ® quartz cells, model 100-QS with a 10 mm optical path, to ensure optimal transmission of UV signals. As.
ACS MIR is an echelle cross dispersion spectrometer dedicated to solar occultation measurements in a spectral range between 2. 2 and 4. 4 μm. Its novel design allows the simultaneous selection of 9 to 25 contiguous diffraction orders within the entire spectral range of the instrument, achieving high resolving power (λ/Δλ∼30,000) in a wide instantaneous spectral range (0. 15-0. 3 μm). The dataset assembled for this study includes all available ACS MIR solar occultation observations made using secondary grating position 4, which allows the selection of ten diffraction orders (215-224) in a spectral range between 2. 65-2. 77 μm (3600 -3770 cm -1 ), encompassing absorption bands of several isotopologues of carbon dioxide and water vapour (see Supplementary Methods). To date, there are 549 secondary grating position 4 solar occultations, which account for approximately 14% of all available ACS MIR observations, covering more than a full Martian year of observations and including more than half of Martian Year 34 (MY34) (L s = 160-360°) and of MY35 (Ls = 0-290°).