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Record W4401927868 · doi:10.1029/2024gl108622

Traveling Light: Arctic Coastal Erosion Releases Mostly Matrix Free, Unprotected Organic Carbon

2024· article· en· W4401927868 on OpenAlexaffabout
Fleur C J van Crimpen, Lina Madaj, Dustin Whalen, Tommaso Tesi, Joost van Genuchten, Lisa Bröder, Timothy I. Eglinton, Negar Haghipour, Jorien E. Vonk

Bibliographic record

VenueGeophysical Research Letters · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsNatural Resources Canada
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsCoastal erosionArcticErosionEnvironmental scienceCarbon fibersOceanographyThe arcticTotal organic carbonMatrix (chemical analysis)GeologyEnvironmental chemistryMaterials scienceGeomorphologyChemistry

Abstract

fetched live from OpenAlex

Abstract The Arctic rapidly warms and sea ice retreats, a large fraction of organic carbon (OC), currently stored in coastal permafrost will be released into the marine system. Once reintroduced into the active carbon cycle, this material will either be decomposed or buried on the shelf depending on its hydrodynamic and chemical properties. Currently, carbon estimates are based on bulk measurements, which does not take the hydrodynamic pathway of different fractions into account. Therefore, eight coastal permafrost locations have been sampled along the Canadian Beaufort Sea Coast, hydrodynamically fractionated and analyzed for their C, N, 13C and 14C content. We found that the matrix‐free fraction (low density <1.8 g/cm3, and high‐density >1.8 g/cm3; <38 μm) account for 77%–98% of the OC. By using a coastal classification combined with field data, our results showed that short coastal segments can become key players in delivering matrix‐free, easily degradable OC to the marine system.

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 score0.128
Threshold uncertainty score0.255

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.294
Teacher spread0.252 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueGeophysical Research Letters→Same topicClimate change and permafrost→French-language works237,207→