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Record W2036246998 · doi:10.1002/asl.162

Cyclone‐induced mixing does not cool SST in the post‐monsoon north Bay of Bengal

2007· article· en· W2036246998 on OpenAlexaboutno aff
Debasis Sengupta, Bharath Raj Goddalehundi, D. S. Anitha

Bibliographic record

VenueAtmospheric Science Letters · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsnot available
FundersNational Aeronautics and Space Administration
KeywordsBayMonsoonBENGALClimatologyTropical cycloneOceanographyEnvironmental scienceSea surface temperatureHydrographyStormCyclone (programming language)Geology

Abstract

fetched live from OpenAlex

Abstract High‐resolution data from the TRMM satellite shows that sea surface temperature (SST) cools by 3 °C under the tracks of pre‐monsoon tropical cyclones in the north Indian Ocean. However, even the strongest post‐monsoon cyclones do not cool the open north Bay of Bengal. In this region, a shallow layer of freshwater from river runoff and monsoon rain caps a deep warm layer. Therefore, storm‐induced mixing is not deep, and it entrains warm subsurface water. It is possible that the hydrography of the post‐monsoon north Bay favours intense cyclones. Copyright © 2007 Royal Meteorological Society

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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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

Citations172
Published2007
Admission routes1
Has abstractyes

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