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Record W4399728594 · doi:10.1038/s43247-024-01508-2

High rates of marine organic carbon burial on the southwest Greenland margin induced by Neoglacial advances

2024· article· en· W4399728594 on OpenAlexafffundabout
Yunfeng Wang, Yves Gélinas, Anne de Vernal, Alfonso Mucci, Estelle Allan, Marit‐Solveig Seidenkrantz, Peter Douglas

Bibliographic record

VenueCommunications Earth & Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversité du Québec à MontréalConcordia UniversityMcGill University
FundersFonds de recherche du Québec – Nature et technologiesHORIZON EUROPE Framework ProgrammeMcGill UniversityEuropean CommissionPinngortitaleriffikDanmarks Frie ForskningsfondAarhus Universitet
KeywordsMargin (machine learning)GeologyPhysical geographyOceanographyTotal organic carbonEarth scienceEnvironmental scienceGeographyEnvironmental chemistryChemistry

Abstract

fetched live from OpenAlex

Abstract Marine sediments in glacially-carved fjords at high latitudes feature high organic carbon (OC) burial rates, but there are fewer data on the role of glacial activity on high-latitude OC burial rates outside of fjords. Here, we investigate the relationship between sediment OC burial rates in the deep troughs and basins of the southwest Greenland shelf and Holocene glacial dynamics. Since the onset of prominent Neoglacial advances ~2500 years ago, the nature of the OC buried in the deep troughs and basins of the shelf was influenced by the glacier-driven increase in sediment accumulation rates (SAR), reactive iron (oxyhydr)oxide concentrations and fine-grain sediment, while OC burial rates were primarily enhanced by increasing SAR. Peak OC burial rates (~18.5 ± 5.7 g m −2 a −1 ) in the deep troughs and basins of the shelf during the past ~1300 years are comparable to those of many high-latitude fjords, and the inferred total annual OC burial in these trough and basin areas is equivalent to ~5% of the annual CO 2 uptake by the Labrador Sea deep convection.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.226
Teacher spread0.211 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations10
Published2024
Admission routes3
Has abstractyes

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