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Record W2096337205 · doi:10.3137/ao.420303

Variability of surface heat flux over the Indian Ocean

2004· article· en· W2096337205 on OpenAlexvenueno aff
Hiroyuki Tomita, Masahisa Kubota

Bibliographic record

VenueATMOSPHERE-OCEAN · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersMinistry of Education, Culture, Sports, Science and Technology
KeywordsLatent heatHeat fluxFlux (metallurgy)ClimatologyAtmospheric sciencesShortwaveShortwave radiationEnvironmental scienceAtmosphere (unit)Sensible heatVariation (astronomy)RadiationHeat transferGeologyMeteorologyGeographyMaterials sciencePhysicsRadiative transferAstrophysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract The variability of surface heat flux over the Indian Ocean is investigated using in situ observational data. First, the Indian Ocean is divided into eight regions and the power spectra of surface heat fluxes are calculated for each region. Consequently it is shown that the surface heat flux over the Indian Ocean has three characteristic timescales: (1) the high frequency timescale (periods shorter than 20 months), (2) the middle frequency timescale (periods between 20 and 60 months), and (3) the low frequency timescale (periods longer than 60 months). Seasonal variation is dominant for the high frequency timescale, with shortwave radiation and the latent heat flux being the principal components of the variability at this timescale. Furthermore, the seasonal variation can be divided into three patterns depending on the region. The variation of shortwave radiation and the latent heat flux are also dominant in the middle‐frequency timescale. In some regions, heat flux variation for this timescale appears to be associated with El Niño. For the low‐frequency timescale, the latent heat flux is dominant. It should be noted that the heat flux from the ocean to the atmosphere has clearly increased since the late 1970s as a result of increases in wind speeds and the specific humidity difference.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.996

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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.

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

Citations18
Published2004
Admission routes1
Has abstractyes

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