On extracting high-frequency tidal variability from HF radar data in the northwestern Bay of Bengal
Bibliographic record
Abstract
First results from a systematic harmonic analysis of HF radar (HFR) derived ocean surface current observations in the northwestern Bay of Bengal (BoB) during 2010 is presented. The daily-averaged HFR currents compared reasonably well to composite daily surface currents from multiple satellites with correlation coefficient of 0.90 (0.69) for zonal (meridional) component. A set of sequential daily currents demonstrated sustained northward (southward) alongshore flow during February–April (October–December) with peak magnitude of about 1.8 (1.2) m/s. On tidal scales, harmonic analyses of zonal and meridional components at nearshore and offshore locations indicated that among semi-diurnal tidal components, M2 dominates over S2 and N2; time-scales of which were verified from available coastal tide gauges nearby. Amplitudes of semi-major axis for M2 and K1 tidal ellipses are 7.16 (6.13) and 4.02 (3.30) cm/s in nearshore (offshore) location indicating relatively stronger tidal currents in nearshore location. Finally, significant shallow water constituents S4, MS4 and M3 (M4, 2SM6 and M6) at nearshore (offshore) location are identified, which are due to non-linear interaction of tidal currents with bathymetry. Both semi-diurnal and shallow water tidal currents show dominance along isobaths in offshore region, which turn progressively across-isobath as they move nearshore.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".