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Record W2599921150

The marine carbon cycle of the Arctic Ocean: Some thoughts about the controls on air-sea CO2 exchanges and responses to ocean acidification

2010· article· en· W2599921150 on OpenAlexaboutno aff
Jeremy T. Mathis, N. R. Bates

Bibliographic record

VenueePrints Soton (University of Southampton) · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsArctic sea ice declineSea iceArctic geoengineeringOceanographyArctic ice packEnvironmental scienceArcticBiogeochemical cycleCarbon cycleIce-albedo feedbackClimatologyCryosphereGlobal warmingClimate changeDrift iceEcosystemGeologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Introduction The Arctic Ocean and the shallow continental margins that surround it (Fig. 1) play an important and likely increasing role in the global freshwater cycle, Atlantic overturning circulation, and biogeochemical cycling of carbon, nutrients, and gases such as carbon dioxide (CO2) and methane. The region is particularly sensitive to atmosphere-ocean-sea-ice forcing and feedbacks and ecosystem changes associated with warming temperatures and sea-ice loss. Numerous studies over the last decade have shown that warming and increased sea-ice loss is occurring in the Arctic and the IPCC Fourth Assessment reported that average Arctic temperatures have increased over the last century at nearly twice the global average and since 1978, sea-ice extent decreased on average by 2.7% per decade. However, over the last several years, the pace of decline has accelerated beyond model predictions and in summer 2007, sea-ice extent declined by 20-25% with an additional loss of ~1.5 million km2. Much of this loss occurred over the deep Makarov and Canada Basins of the Arctic Ocean (Fig. 1). Although ice extent rebounded in 2008 and 2009, the duration of ice-free conditions in the Arctic continues to increase.

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)0.001
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 score0.009
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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Same venueePrints Soton (University of Southampton)Same topicArctic and Antarctic ice dynamicsFrench-language works237,207