Yellow Sea ocean‐acoustic solitary wave modeling studies
Bibliographic record
Abstract
This study is in an area south of the Shandong peninsula, near the region where Zhou et al. (1991) observed anomalous drops in acoustical intensity. Solitary wave generation and propagation simulations are performed using the Lamb (1994) nonhydrostatic model. The model simulations show that, for summer conditions, the existing semi‐diurnal tidal flow over the topographic variations formed internal bores and solitary waves. For the Shandong area, we analyzed summer observations from Synthetic Aperture Radar (SAR) that tracked solitary wave trains from their surface roughness signatures. The images contained seven events consisting of internal bores and solitary waves that traveled in a well‐defined direction for 2.5 days. The origin of the trains appeared at a well‐defined point along a steep topographic drop. The SAR observations guided and tuned the model simulations, by comparing spectra of observed and modeled wavelengths. The tuned model yields wavelengths within factors of 2, or less, of those derived from SAR data. Wavelength and amplitude dispersion analysis showed two dispersion regimes. Modeled phase speeds were at the lower limit of phase speeds deduced from SAR data, from about 0.8 to 1.0 m/s. Acoustical intensity calculations in the presence of solitary wave trains will be undertaken in a subsequent paper using a parabolic equation acoustical model along the path of solitary wave train propagation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".