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Record W1904185185 · doi:10.1029/2011gl048204

Temperature signature of high latitude Atlantic boundary currents revealed by marine mammal‐borne sensor and Argo data

2011· article· en· W1904185185 on OpenAlexafffundabout
Jeremy P. Grist, Simon A. Josey, Lars Boehme, Michael P. Meredith, Fraser Davidson, Garry B. Stenson, Mike O. Hammill

Bibliographic record

VenueGeophysical Research Letters · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsFisheries and Oceans Canada
FundersOffice of Polar ProgramsFisheries and Oceans CanadaNatural Environment Research CouncilSight Research UKSociety for Marine Mammalogy
KeywordsArgoMarine mammalOceanographySea surface temperatureLatitudeGeologyBoundary currentClimatologyOcean currentFisheryGeodesyBiology

Abstract

fetched live from OpenAlex

Results from the development and analysis of a novel temperature dataset for the high latitude North‐West Atlantic are presented. The new 1° gridded dataset (“ATLAS”) has been produced from about 13,000 Argo and 48,000 marine mammal (hooded seal, harp seal, grey seal and beluga) profiles spanning 2004–8. These data sources are highly complementary as marine mammals greatly enhance shelf region coverage where Argo floats are absent. ATLAS reveals distinctive boundary current related temperature minima in the Labrador Sea (−1.1°C) and at the east Greenland coast (1.8°C), largely absent in the widely‐used Levitus'09 and EN3v2a datasets. The ATLAS 0–500 m average temperature is lower than Levitus'09 and EN3v2a by up to 3°C locally. Differences are strongest from 0–300 m and persist at reduced amplitude from 300–500 m. Our results clearly reveal the value of marine mammal‐borne sensors for a reliable description of the North‐West Atlantic at a time of rapid change.

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)0.001
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.031
GPT teacher head0.264
Teacher spread0.234 · 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

Citations25
Published2011
Admission routes3
Has abstractyes

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