Characteristics Of Southern Ocean Swells Propagating Into The Northern Indian Ocean
Bibliographic record
Abstract
Experiments were carried out utilizing a numerical wave model to study the properties of swells propagating into the northern Indian Ocean from the Southern Ocean. It is a well-known fact that the northern Indian Ocean is distinguished from other oceans primarily by the annual reversal of wind twice during the southwest and northeast monsoon seasons. In this context a comparative study has been conducted regarding swell propagation for the months of July and December of 2015. The propagation of swell waves from Southern Ocean and South Indian Ocean to North Indian Ocean is studied in the present work. The complicated phenomena of Southern Ocean swells spreading into the northern Indian Ocean have important ramifications for coastal areas and marine industries. This research uses a combination of satellite measurements, computational models, and statistical studies to examine the properties of these swells. To capture the variability and patterns of Southern Ocean swell propagation into the northern Indian Ocean, it is analyzed data spanning several years. In the Arabian Sea and Bay of Bengal regions the swell waves follow the wind direction in July representing the southwest monsoons whereas they move opposite to the wind direction in December representing the northeast monsoons. The swells generated between 400S-600S take 6-8 days to reach the northern Indian Ocean and the speed calculated is nearly 1100km per day
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".