Spectral Properties of Cyclone-Generated Waves in Deep and Shallow Water
Bibliographic record
Abstract
Juan is recorded as one of the most damaging storms in the modern history of Nova Scotia, Canada. In this paper, the spectral evolution characteristics of waves generated by hurricane Juan are studied, based on the observed 1D wave spectra along Juan’s track in deep open ocean waters (buoy 44137) and the 2D wave spectra in shallow coastal waters at the directional waverider (DWR) location. Valuable results are obtained for cyclone-generated wave spectral properties, in both deep and shallow waters. In deep water, as illustrated at buoy 44137, the spectral variation, spectra pattern development, spectral peak frequency, cut-off frequency coefficient and high frequency spectral tail of the wave spectra are analyzed, before, during and after the cyclone’s passing. Thus, the spectral variation characteristics during the entire cyclone processe are obtained. Properties of the high frequency spectral tail are discussed, using average frequency and peak frequency as the cut-off frequency parameters under different cut-off coefficient conditions, respectively. We suggest reasonable values for the cut-off frequency parameter. Cyclone-generated 2D wave spectra in shallow water (at DWR location) are investigated, shoaling effects, 2D spectral pattern variations, swell and wind waves spectral evolution. Our study shows the invalidity of presently accepted spectral formulae, in describing cyclone-generated waves.
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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 | 0.001 |
| 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".