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Record W3118115925 · doi:10.5194/essd-13-2561-2021

CASCADE – The Circum-Arctic Sediment CArbon DatabasE

2021· article· en· W3118115925 on OpenAlexafffund
Jannik Martens, Evgeny A Romankevich, Igor Semiletov, Birgit Wild, Bart E. van Dongen, Jorien E. Vonk, Tommaso Tesi, Natalia Shakhova, Oleg Dudarev, Denis Kosmach, A. A. Vetrov, L. I. Lobkovsky, N. A. Belyaev, Robie W. Macdonald, Anna J. Pieńkowski, Timothy I. Eglinton, Negar Haghipour, Salve Dahle, Michael L. Carroll, Emmelie K. L. Åström, Jacqueline M. Grebmeier, Lee W. Cooper, Göran Possnert, Örjan Gustafsson

Bibliographic record

VenueEarth system science data · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsFisheries and Oceans Canada
FundersH2020 European Research CouncilNorges ForskningsrådRussian Academy of SciencesNational Research Council CanadaAard- en Levenswetenschappen, Nederlandse Organisatie voor Wetenschappelijk OnderzoekNatural Environment Research CouncilGovernment Council on Grants, Russian FederationRussian Science FoundationVetenskapsrådetSight Research UKStatoilKnut och Alice Wallenbergs Stiftelse
KeywordsTerrigenous sedimentArcticBiogeochemical cycleOceanographySedimentEnvironmental scienceTotal organic carbonGeologySedimentary rockCoringDatabaseEarth sciencePhysical geographyEnvironmental chemistryGeochemistryChemistryPaleontologyDrillingGeography

Abstract

fetched live from OpenAlex

Abstract. Biogeochemical cycling in the semi-enclosed Arctic Ocean is strongly influenced by land–ocean transport of carbon and other elements and is vulnerable to environmental and climate changes. Sediments of the Arctic Ocean are an important part of biogeochemical cycling in the Arctic and provide the opportunity to study present and historical input and the fate of organic matter (e.g., through permafrost thawing). Comprehensive sedimentary records are required to compare differences between the Arctic regions and to study Arctic biogeochemical budgets. To this end, the Circum-Arctic Sediment CArbon DatabasE (CASCADE) was established to curate data primarily on concentrations of organic carbon (OC) and OC isotopes (δ13C, Δ14C) yet also on total N (TN) as well as terrigenous biomarkers and other sediment geochemical and physical properties. This new database builds on the published literature and earlier unpublished records through an extensive international community collaboration. This paper describes the establishment, structure and current status of CASCADE. The first public version includes OC concentrations in surface sediments at 4244 oceanographic stations including 2317 with TN concentrations, 1555 with δ13C-OC values and 268 with Δ14C-OC values and 653 records with quantified terrigenous biomarkers (high-molecular-weight n-alkanes, n-alkanoic acids and lignin phenols). CASCADE also includes data from 326 sediment cores, retrieved by shallow box or multi-coring, deep gravity/piston coring, or sea-bottom drilling. The comprehensive dataset reveals large-scale features of both OC content and OC sources between the shelf sea recipients. This offers insight into release of pre-aged terrigenous OC to the East Siberian Arctic shelf and younger terrigenous OC to the Kara Sea. Circum-Arctic sediments thereby reveal patterns of terrestrial OC remobilization and provide clues about thawing of permafrost. CASCADE enables synoptic analysis of OC in Arctic Ocean sediments and facilitates a wide array of future empirical and modeling studies of the Arctic carbon cycle. The database is openly and freely available online (https://doi.org/10.17043/cascade; Martens et al., 2021), is provided in various machine-readable data formats (data tables, GIS shapefile, GIS raster), and also provides ways for contributing data for future CASCADE versions. We will continuously update CASCADE with newly published and contributed data over the foreseeable future as part of the database management of the Bolin Centre for Climate Research at Stockholm University.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.074
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0130.021
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.014

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.078
GPT teacher head0.272
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations58
Published2021
Admission routes2
Has abstractyes

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