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Record W2104702824 · doi:10.1093/icesjms/fsp094

On the effect of the North Atlantic Oscillation on temperature and salinity of the subpolar North Atlantic intermediate and deep waters

2009· article· en· W2104702824 on OpenAlexaboutno aff
Artem Sarafanov

Bibliographic record

VenueICES Journal of Marine Science · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsOcean gyreNorth Atlantic oscillationNorth Atlantic Deep WaterThermohaline circulationSalinityOceanographyAtlantic multidecadal oscillationCircumpolar deep waterWater massTemperature salinity diagramsClimatologyGeologyWater columnSubtropicsFishery

Abstract

fetched live from OpenAlex

Abstract Sarafanov, A. 2009. On the effect of the North Atlantic Oscillation on temperature and salinity of the subpolar North Atlantic intermediate and deep waters. – ICES Journal of Marine Science, 66: 1448–1454. The close relationship between the observed water mass properties and the winter North Atlantic Oscillation (NAO) index (1950–2000s; r2 ≈ 0.65) implies that changes in the NAO-related atmospheric forcing may account for up to two-thirds of thermohaline changes at the intermediate and deep levels in the subpolar North Atlantic on a decadal time-scale. Persistent NAO decline (amplification) results in increase (decrease) in temperature and salinity in the intermediate–deep water column. A general mechanism explaining the close link between the NAO and coherent decadal changes in the intermediate and deep-water temperature and salinity in the region is inferred from the observed changes in the regional circulation and water mass properties. Two factors dominate this link: (i) intensity of convection in the Labrador Sea controlling injection of relatively cold freshwater into the intermediate layer, and (ii) zonal extension of the Subpolar Gyre that regulates the relative contribution of cold fresh subpolar water and warm saline subtropical water to the deep-water formation.

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)0.000
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 score0.005
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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

Citations107
Published2009
Admission routes1
Has abstractyes

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