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Record W2967318772 · doi:10.1029/2019gl083547

The Ocean CO<sub>2</sub> Sink in the Canadian Arctic Archipelago: A Present‐Day Budget and Past Trends Due to Climate Change

2019· article· en· W2967318772 on OpenAlexafffundabout
Mohamed Ahmed, Brent Else

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Calgary
FundersFaculty of Arts, University of CalgarySocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaChinese Academy of Agricultural SciencesCanadian HIV Trials Network, Canadian Institutes of Health ResearchNetworks of Centres of Excellence of CanadaArcticNet
KeywordsArcticSink (geography)Environmental scienceArctic sea ice declineSea iceArchipelagoClimatologyArctic dipole anomalyArctic geoengineeringArctic ice packOceanographyOpen waterAtmospheric sciencesAntarctic sea iceGeologyGeography

Abstract

fetched live from OpenAlex

Abstract Arctic shelf seas are highly heterogeneous, making it difficult to accurately account for their role in regional and global air‐sea CO2 exchange budgets. Here we estimate the CO2 sink in the Canadian Arctic Archipelago (CAA) based on empirical relationships that account for spatiotemporal variations in the sea surface partial pressure of CO2 (pCO2sw) as a function of seasonal sea ice cycles. During the open water season from 2010 to 2016, the CAA acted as a net oceanic sink with an average CO2 flux of −7.7 ± 4 Tg C/year. This sink is significantly smaller than previous estimates for the CAA, emphasizing the importance of properly accounting for seasonal and spatial variability on Arctic shelves. Applying our analysis to a 37‐year record of sea ice conditions, we calculate an increase in the open water CO2 sink by ~150% (a trend of ~ −1.3 Tg C/decade), associated with sea ice loss and higher wind speeds.

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 score0.027
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
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 head0.020
GPT teacher head0.257
Teacher spread0.237 · 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

Citations13
Published2019
Admission routes3
Has abstractyes

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