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Record W1547681715 · doi:10.1002/grl.50268

Further observations of a decreasing atmospheric CO<sub>2</sub> uptake capacity in the Canada Basin (Arctic Ocean) due to sea ice loss

2013· article· en· W1547681715 on OpenAlexafffundabout
Brent Else, R. J. Galley, Bruno Lansard, David G. Barber, Kristina A. Brown, Lisa A. Miller, Alfonso Mucci, Tim Papakyriakou, Jean‐Éric Tremblay, Søren Rysgaard

Bibliographic record

VenueGeophysical Research Letters · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversité LavalFisheries and Oceans CanadaMcGill UniversityUniversity of British ColumbiaUniversity of Manitoba
FundersDivision of Arctic SciencesSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of ManitobaArcticNetCanada Research ChairsCanadian Institutes of Health Research
KeywordsSink (geography)Sea iceArcticArctic ice packEnvironmental scienceAtmospheric sciencesClimatologyCryosphereStructural basinAntarctic sea iceCanada BasinGeologyOceanographyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Abstract Using data collected in 2009, we evaluated the potential for the southeastern Canada Basin (Arctic Ocean) to act as an atmospheric CO 2 sink under the summertime ice‐free conditions expected in the near future. Beneath a heavily decayed ice cover, we found surprisingly high p CO 2sw (~290–320 µatm), considering that surface water temperatures were low and the influence of ice melt was strong. A simple model simulating melt of the remaining ice and exposure of the surface water for 100 days revealed a weak capacity for atmospheric CO 2 uptake (mean flux: −2.4 mmol m −2 d −1 ), due largely to warming of the shallow mixed layer. Our results confirm a previous finding that the Canada Basin is not a significant sink of atmospheric CO 2 under summertime ice‐free conditions and that increased ventilation of the surface mixed layer due to sea ice loss is weakening the sink even further.

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)0.001
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 score0.447
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.237
Teacher spread0.205 · 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

Citations69
Published2013
Admission routes3
Has abstractyes

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