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Record W1968973672 · doi:10.1029/2006gl027028

An increasing CO<sub>2</sub> sink in the Arctic Ocean due to sea‐ice loss

2006· article· en· W1968973672 on OpenAlexaboutno aff
Nicholas R. Bates, S. Bradley Moran, Dennis A. Hansell, Jeremy T. Mathis

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsArctic sea ice declineSea iceOceanographyArctic geoengineeringArctic ice packArcticEnvironmental scienceSink (geography)SeawaterCryosphereArctic dipole anomalyCanada BasinAntarctic sea iceGeologyContinental shelfSea ice thicknessClimatologyGeography

Abstract

fetched live from OpenAlex

The Arctic Ocean and adjacent continental shelf seas such as the Chukchi and Beaufort Seas are particularly sensitive to long‐term change and low‐frequency modes of atmosphere‐ocean‐sea‐ice forcing. The cold, low salinity surface waters of the Canada Basin of the Arctic Ocean are undersaturated with respect to CO 2 in the atmosphere and the region has the potential to take up atmospheric CO 2 , although presently suppressed by sea‐ice cover. Undersaturated seawater CO 2 conditions of the Arctic Ocean are maintained by export of water with low dissolved inorganic carbon content and modified by intense seasonal shelf primary production. Sea‐ice extent and volume in the Arctic Ocean has decreased over the last few decades, and we estimate that the Arctic Ocean sink for CO 2 has tripled over the last 3 decades (24 Tg yr −1 to 66 Tg yr −1 ) due to sea‐ice retreat with future sea‐ice melting enhancing air‐to‐sea CO 2 flux by ∼28% per decade.

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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.265
Teacher spread0.249 · 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

Citations178
Published2006
Admission routes1
Has abstractyes

Explore more

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