Variability of surface heat flux over the Indian Ocean
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".