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Record W3173733970 · doi:10.5194/egusphere-egu21-5401

Patterns of terrestrial carbon distribution in the Arctic Ocean deduced from the Circum-Arctic Sediment CArbon DatabasE (CASCADE)

2021· article· en· W3173733970 on OpenAlexaboutno aff
Jannik Martens, Birgit Wild, Igor Semiletov, Oleg Dudarev, Örjan Gustafsson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsArcticSedimentCarbon cyclePermafrostOceanographyTotal organic carbonEnvironmental scienceCanada BasinCarbon fibersGeologyChemistryEnvironmental chemistryEcologyEcosystemPaleontologyBiology

Abstract

fetched live from OpenAlex

Climate change is expected to affect the release of organic carbon (OC) from Arctic permafrost systems and other terrestrial deposits. In addition to greenhouse gas emissions on land, a fraction of the organic matter is liberated to the aquatic cycle and migrates to the Arctic Ocean where it is either degraded or buried in sediments, foremost on the vast continental shelves. The Arctic Ocean basin represents a large footprint for contemporary and past land-ocean transport of terrestrial OC. To this end, the Circum-Arctic Sediment CArbon DatabasE (CASCADE; https://doi.org/10.5194/essd-2020-401) was established to curate and harmonize data on OC and total nitrogen concentrations, carbon isotopes (δ13C, Δ14C) and terrestrial biomarkers (long-chain n-alkanes, n-alkanoic acids and lignin phenols) in an openly accessible data collection. CASCADE was populated using carbon data from both published records and unpublished data from a large community collaboration. The first release of CASCADE includes observations at thousands of oceanographic stations distributed over the shelves and the central basins of the Arctic Ocean, and also includes hundreds of sediment cores, representing a range of time scales (decadal to orbital). Mapping CASCADE data provides an overview to start deducing sources, pathways and deposition of carbon in different regions of the Arctic Ocean. Dual-isotope (δ13C, Δ14C) source apportionment of OC and 210Pb dating of centennial-scale sediment cores permit quantitative analysis of sequestration of carbon transported from permafrost systems and other deposits to the Arctic Ocean. Preliminary results suggest that surface soils (incl. permafrost active layer) are the dominating terrestrial carbon source to Circum-Arctic shelf sediments. The second largest terrestrial source are Pleistocene ice-rich permafrost compartments (Ice Complex Deposits), which stretch along coastlines of the Laptev, East Siberian, Chukchi and Beaufort Seas and are highly vulnerable to coastal erosion and thermal collapse in a warming climate. Climate change is likely to cause permafrost thawing by further deepening of the seasonal active layer and accelerated coastal erosion of permafrost, as well as disturbance of the vast boreal peatlands. CASCADE provides an integrated perspective and benchmark for lateral carbon remobilization and will fuel further empirical and modelling studies of Arctic biogeochemical cycles.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.166
Threshold uncertainty score0.330

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.009
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2021
Admission routes1
Has abstractyes

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