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Record W1909479374 · doi:10.1029/2009gb003503

Glacial to postglacial transformation of organic input pathways in Arctic Ocean basins

2009· article· en· W1909479374 on OpenAlexaff
Mark B. Yunker, Robie W. Macdonald, L R Snowdon

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsGlacial periodGeologyOceanographyTerrigenous sedimentDeglaciationArcticSea iceLast Glacial MaximumHoloceneGlacial landformPhysical geographySedimentGeomorphologyMoraineGeography

Abstract

fetched live from OpenAlex

The Arctic Ocean is undergoing rapid loss in ice cover with yet unknown consequences for the cycling of organic material. Here we examine persistent terrigenous (land‐based) alkane and polycyclic aromatic hydrocarbons with vascular plant, combustion, and petrogenic sources in seven cores collected from all major basins of the Arctic Ocean for insight as to how organic cycling at the Last Glacial Maximum compares to the present day. We find only modest changes between the glacial and postglacial sediments for atmospherically transported hydrocarbon biomarkers, demonstrating that glacial sea ice was not a barrier to atmospheric inputs. In stark contrast, particle‐associated biomarkers were captured strongly at basin edges during the glacial period and much more evenly transported across basins during the postglacial period. Evidently the capture of organic matter shifted from the slopes to the shelves as the latter flooded during the Holocene, and the Transpolar Drift and Beaufort Gyre evolved from minor carriers of plant detritus from the glacial ocean margins to major modern transporters of shelf sediment to the basins. This suggests that changes in organic transport currently accompanying the loss of sea ice are likely to be very different from those that occurred at the end of the last glacial period.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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.

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

Citations9
Published2009
Admission routes1
Has abstractyes

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