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Record W3155815203 · doi:10.1029/2020gl091814

Multidecadal Variability in Mediterranean Sea Surface Temperature and Its Sources

2021· article· en· W3155815203 on OpenAlexaff
Xiaoqin Yan, Youmin Tang

Bibliographic record

VenueGeophysical Research Letters · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Northern British Columbia
FundersHohai University
KeywordsAtlantic multidecadal oscillationNorth Atlantic oscillationClimatologyMediterranean climateMediterranean seaForcing (mathematics)Sea surface temperatureEnvironmental scienceLagOceanographyClimate changeAtlantic Equatorial modeGeographyGeology

Abstract

fetched live from OpenAlex

Abstract The multidecadal variability in Mediterranean Sea surface temperature (MSMV) exerts important climate impacts on both the Mediterranean region and remote areas at hemispheric scales and has long been identified in previous studies. However, its key region and source are still unclear. For the first time, we show that the key region of the MSMV is in the eastern Mediterranean, where the MSMV can persist throughout the year. The MSMV in the central and western Mediterranean occurs mainly in summer. Comparison of the North Atlantic Oscillation (NAO) and Atlantic Multidecadal Variability (AMV) indices shows that the cumulative NAO index has a better consistency with the MSMV, suggesting that the MSMV most likely results from the cumulative effect of NAO atmospheric forcing on ocean circulation in the Mediterranean Sea. The stable lag relationship between the cumulative NAO index and the MSMV provides a natural indicator for the decadal prediction of the MSMV.

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)0.001
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.040
GPT teacher head0.310
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 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

Citations11
Published2021
Admission routes1
Has abstractyes

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