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Record W2566479556 · doi:10.1002/2016gl071668

Further intensification of deep convection in the Labrador Sea in 2016

2016· article· en· W2566479556 on OpenAlexaffabout
Igor Yashayaev, John W. Loder

Bibliographic record

VenueGeophysical Research Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsArgoClimatologyNorth Atlantic oscillationGeologyOceanographyForcing (mathematics)Shutdown of thermohaline circulationNorth Atlantic Deep WaterThermohaline circulationGeneral Circulation ModelConvectionClimate changeGeographyMeteorology

Abstract

fetched live from OpenAlex

Abstract There has been a progressive deepening of winter convection in the Labrador Sea since 2012, with the individual profile maximum depth exceeding 1800 m since 2014 and reaching 2100 m in 2016. This increase, during repeated positive phases of the winter North Atlantic Oscillation (NAO), resembles that during the formation of the record depth (2500 m) Labrador Sea Water (LSW) class in 1987–1994, attributed to repeated positive NAO forcing having provided critical preconditioning. The 2012–2016 LSW class is one of the deepest and most persistent ever observed (back to 1938). Year‐round observations from profiling Argo floats since 2002 complemented by annual surveys are providing novel information on the seasonal‐to‐decadal evolution of LSW, such as its variable density, the recent multiyear preconditioning, and its 2016 density being the highest since the mid‐1990s. These findings should help international observation programs and numerical model studies investigating LSW influences on the subpolar North Atlantic and Atlantic Meridional Overturning Circulation.

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 score0.028
Threshold uncertainty score0.056

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 science0.0000.001
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 head0.023
GPT teacher head0.264
Teacher spread0.241 · 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

Citations190
Published2016
Admission routes2
Has abstractyes

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