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Record W3042816621 · doi:10.1029/2020gl088561

Nearshore Zone Dynamics Determine Pathway of Organic Carbon From Eroding Permafrost Coasts

2020· article· en· W3042816621 on OpenAlexaboutno aff
Dirk Jong, Lisa Bröder, George Tanski, Michael Fritz, Hugues Lantuit, Tommaso Tesi, Negar Haghipour, Timothy I. Eglinton, Jorien E. Vonk

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersEuropean Research CouncilHorizon 2020 Framework Programme
KeywordsPermafrostTotal organic carbonArcticOceanographyEnvironmental scienceSubmarine pipelineSedimentThermokarstGeologyRadiocarbon datingSettlingHydrology (agriculture)Environmental chemistryGeomorphologyEnvironmental engineeringPaleontologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Collapse of permafrost coasts delivers large quantities of particulate organic carbon (POC) to Arctic coastal areas. With rapidly changing environmental conditions, sediment and organic carbon (OC) mobilization and transport pathways are also changing. Here, we assess the sources and sinks of POC in the highly dynamic nearshore zone of Herschel Island‐Qikiqtaruk (Yukon, Canada). Our results show that POC concentrations sharply decrease, from 15.9 to 0.3 mg L−1, within the first 100–300 m offshore. Simultaneously, radiocarbon ages of POC drop from 16,400 to 3,600 14C years, indicating rapid settling of old permafrost POC to underlying sediments. This suggests that permafrost OC is, apart from a very narrow resuspension zone (<5 m water depth), predominantly deposited in nearshore sediments. While long‐term storage of permafrost OC in marine sediments potentially limits biodegradation and its subsequent release as greenhouse gas, resuspension of fine‐grained, OC‐rich sediments in the nearshore zone potentially enhances OC turnover.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.363

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.275
Teacher spread0.210 · 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

Citations51
Published2020
Admission routes1
Has abstractyes

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Same venueGeophysical Research LettersSame topicClimate change and permafrostFrench-language works237,207