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Record W2162602390 · doi:10.1002/jgrc.20326

Forcing mechanisms of heat content variations in the Yellow Sea

2013· article· en· W2162602390 on OpenAlexaff
Hao Wei, Chengyi Yuan, Youyu Lu, Zhihua Zhang, Xiaofan Luo

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsClimatologyEnvironmental scienceForcing (mathematics)Shortwave radiationHindcastMonsoonLatent heatAtmospheric sciencesSensible heatVariation (astronomy)Ocean heat contentShortwaveSea surface temperatureMeteorologyGeologyGeographyRadiation

Abstract

fetched live from OpenAlex

Forcing mechanisms of heat content variations in the Yellow Sea (YS) are studied through analysis of a hindcast simulator for 1958–2007 using a two–way nested global—Northwest Pacific model. During the cooling season (September to February of next year), changes in heat content integrated over the YS are primarily caused by variations in latent and sensible heat fluxes at surface, which can be further related to variations of the East Asian Winter Monsoon and the Arctic Oscillation. The lateral heat transport by the Yellow Sea Warm Current (YSWC) contributes to heat content variation in the deep region. Variation of the YSWC can be related to wind variation over the YS. During the warming season (March to August), variation in the changes of heat content in the upper layer is primarily caused by variation in shortwave radiation at surface, which can be related to variation in the atmospheric pressure system of the Western Pacific Subtropical High that influences the YS in summer.

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 score0.078
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.282
Teacher spread0.228 · 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
Published2013
Admission routes1
Has abstractyes

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