Bibliographic record
Abstract
Using a one-dimensional model, Mixed Layer Depth (MLD) is simulated for the north Indian Ocean during May and September. The results are verified with the observed values. Surface meteorological and subsurface data were collected from NODC and IDWRs for 1970-1977 period. The model results indicate that its performance in May is somewhat better in the central Arabian Sea and eastern Bay of Bengal. Excess values are simulated in the western Arabian Sea and central equatorial region. The excess values along the western Arabian Sea are due to coastal upwelling which is not accounted for in the model. In September, the model underestimated the MLD over the western Arabian Sea, whereas it highly overestimates off the west coast of India. The eastward transport of colder surface waters from the extreme western Arabian Sea may be responsible for the low simulated values over the western and central Arabian Sea. In this month, excess values are diagnosed over southern Bay of Bengal. This is the region of net heat loss and negative Ekman Pumping Velocity (EPV). Both these forces augment the mixed layer development. In the absence of EPV term in the model, low MLD should have been simulated. In contrast higher values are found. This shows the deviations in MLD are due to relatively higher values of net heat loss
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.986 | 0.986 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".