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Record W2147946072 · doi:10.1357/0022240041446209

Winter conditions in the Irminger Sea observed with profiling floats

2004· article· en· W2147946072 on OpenAlexaboutno aff
Luca Centurioni, W.J. Gould

Bibliographic record

VenueJournal of Marine Research · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
Fundersnot available
KeywordsOceanographyProfiling (computer programming)Environmental scienceClimatologyGeologyComputer science

Abstract

fetched live from OpenAlex

Temperature/salinity profiles collected between 1994 and 2003 with profiling floats in the North Atlantic subpolar gyre are analyzed to investigate the hydrographic conditions in winter in the Irminger Sea. The salinity data can be calibrated against accurate profiles from ships obtained mostly during summer months and the resulting float profile salinity accuracy is of the order of 0.015. Between 1997 and 2003, when the North Atlantic Oscillation (NAO) index was generally low, the potential temperature and salinity of the Labrador Sea Water (LSW) core observed by the floats showed a positive trend, an indication of little or no deep convection. The float data show that in the Irminger Sea the thermal energy of the water column reaches the lowest values south and southwest of Cape Farewell, a place where deep convective events are likely to occur. The geostrophic velocity field at 15 m computed from drifting buoys and satellite measurements of sea level shows, for the same area, mean currents below 0.1 m s 1 and low levels of eddy kinetic energy. These factors, together with recent estimates of winter air-sea heat fluxes as high as 500 W m 2 for this region, are exploited to explore the evolution of the surface mixed layer using several one-dimensional models. The results suggest that the typical thickness of the surface mixed layer at the end of winter is of the order of 400 m. This is similar to observed values from floats.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.008
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.071
GPT teacher head0.311
Teacher spread0.239 · 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

Citations27
Published2004
Admission routes1
Has abstractyes

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