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Record W2125905259 · doi:10.1126/science.1189338

Decrease in the CO <sub>2</sub> Uptake Capacity in an Ice-Free Arctic Ocean Basin

2010· article· en· W2125905259 on OpenAlexaffabout
Wei‐Jun Cai, Liqi Chen, Baoshan Chen, Zhongyong Gao, Sang Heon Lee, Jianfang Chen, Denis Pierrot, Kevin F. Sullivan, Yongchen Wang, Xinping Hu, Wei‐Jen Huang, Yuanhui Zhang, Suqing Xu, Akihiko Murata, Jacqueline M. Grebmeier, E. Peter Jones, Haisheng Zhang

Bibliographic record

VenueScience · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsBedford Institute of Oceanography
Fundersnot available
KeywordsSink (geography)Sea iceEnvironmental scienceArctic ice packArcticOceanographyCarbon dioxide in Earth's atmosphereStructural basinAtmosphere (unit)Arctic sea ice declineDrawdown (hydrology)Arctic geoengineeringThe arcticCarbon dioxideDrift iceCryosphereClimatologyGeologyClimate changeGeographyMeteorologyEcologyGeomorphology

Abstract

fetched live from OpenAlex

It has been predicted that the Arctic Ocean will sequester much greater amounts of carbon dioxide (CO2) from the atmosphere as a result of sea ice melt and increasing primary productivity. However, this prediction was made on the basis of observations from either highly productive ocean margins or ice-covered basins before the recent major ice retreat. We report here a high-resolution survey of sea-surface CO2 concentration across the Canada Basin, showing a great increase relative to earlier observations. Rapid CO2 invasion from the atmosphere and low biological CO2 drawdown are the main causes for the higher CO2, which also acts as a barrier to further CO2 invasion. Contrary to the current view, we predict that the Arctic Ocean basin will not become a large atmospheric CO2 sink under ice-free conditions.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.018
GPT teacher head0.220
Teacher spread0.202 · 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 designSimulation or modeling
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

Citations276
Published2010
Admission routes2
Has abstractyes

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