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Record W2491946443 · doi:10.1201/b18559-35

Estimation of air-sea carbon flux in the Western Arctic Ocean using in-situ and remotely sensed data

2015· book-chapter· en· W2491946443 on OpenAlexaboutno aff
Suqing Xu, L. Chen, H. Chen

Bibliographic record

VenueEnvironmental Science and Information Application Technology · 2015
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsIn situCarbon fluxArcticFlux (metallurgy)The arcticEnvironmental scienceOceanographyRemote sensingClimatologyGeologyMeteorologyGeographyMaterials scienceEcologyBiology

Abstract

fetched live from OpenAlex

Affected by global warming and rapid sea ice change, the Western Arctic Ocean is a strong potential carbon sink. In order to get a high resolution basin-scale estimate of the air-sea flux of CO 2 and carbon uptake in the Western Arctic Ocean from 180°E to 135°W, 65°N to 85°N, we applied an extrapolation method. Empirical relationship between marine partial pressure of carbon dioxide ( p CO 2 sw ), Sea Surface Temperature (SST) and salinity were derived from underway measurements collected during the 3 rd CHINARE, in August 2008. p CO 2 sw was computed from remotely sensed SST, and salinity produced by remotely sensed Colored Dissolved Organic Matter (CDOM). Air-sea CO 2 flux was calculated from the air-sea difference of p CO 2 and remotely sensed wind speed. Results showed that in August, 2008, the monthly CO 2 flux was highest in the Chukchi Sea, secondly in the Beaufort Sea and then in the Canada Basin. In August, the Western Arctic Ocean was a net atmospheric sink for CO 2 and carbon absorption was 4.8 TgC.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.214
Teacher spread0.200 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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