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Record W2078170471 · doi:10.1029/2008gl036162

Enhanced production of Labrador Sea Water in 2008

2009· article· en· W2078170471 on OpenAlexaffabout
Igor Yashayaev, John W. Loder

Bibliographic record

VenueGeophysical Research Letters · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersDivision of Ocean Sciences
KeywordsArgoOceanographyConvectionAtmosphere (unit)ClimatologyGeologyEnvironmental scienceThermohaline circulationAtmospheric sciencesGeographyMeteorology

Abstract

fetched live from OpenAlex

A May 2008 oceanographic survey of the Labrador Sea and recent Argo float profiles have revealed that convective overturning extended to a depth of about 1600 m during the winter of 2008, resulting in the production of a large “year class” of Labrador Sea Water. This convection was the deepest since 1994, and substantially exceeded the convection to 500–1100 m in the past few years in both the Labrador and Irminger Seas. The resultant 0.2°C cooling of the intermediate‐depth waters in the Labrador Sea has disrupted a steady warming of these waters since 1994. The cumulative heat loss from the ocean to the atmosphere during the 2007–2008 cooling season was the largest since the mid‐1990s and exceeded the 2000–2007 mean by about 50%. This indicates that enhanced atmospheric cooling, apparently associated with below‐normal air temperatures in the region, was the predominant factor contributing to the enhanced LSW production in 2008.

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 score0.019
Threshold uncertainty score0.038

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 communication0.0010.000
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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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

Citations169
Published2009
Admission routes2
Has abstractyes

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