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Record W2521337834 · doi:10.1002/2015jc011607

Atmospheric forcing during active convection in the <scp>L</scp>abrador <scp>S</scp>ea and its impact on mixed‐layer depth

2016· article· en· W2521337834 on OpenAlexafffundabout
Lena M. Schulze Chretien, Robert S. Pickart, G. W. K. Moore

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of SouthamptonNational Science Foundation
KeywordsMixed layerStructural basinClimatologyGeologyForcing (mathematics)StormHydrographyConvectionAtmospheric sciencesWinter stormOceanographyMeteorologyGeographyGeomorphology

Abstract

fetched live from OpenAlex

Abstract Hydrographic data from the Labrador Sea collected in February–March 1997, together with atmospheric reanalysis fields, are used to explore relationships between the air‐sea fluxes and the observed mixed‐layer depths. The strongest winds and highest heat fluxes occurred in February, due to the nature and tracks of the storms. While greater numbers of storms occurred earlier and later in the winter, the storms in February followed a more organized track extending from the Gulf Stream region to the Irminger Sea where they slowed and deepened. The canonical low‐pressure system that drives convection is located east of the southern tip of Greenland, with strong westerly winds advecting cold air off the ice edge over the warm ocean. The deepest mixed layers were observed in the western interior basin, although the variability in mixed‐layer depth was greater in the eastern interior basin. The overall trend in mixed‐layer depth through the winter in both regions of the basin was consistent with that predicted by a 1‐D mixed‐layer model. We argue that the deeper mixed layers in the west were due to the enhanced heat fluxes on that side of the basin as opposed to oceanic preconditioning.

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)0.002
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 score0.124
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.295
Teacher spread0.270 · 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

Citations23
Published2016
Admission routes3
Has abstractyes

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