Anomalous signal loss in the Yellow Sea, revisited: Coupling the acoustics with model-generated oceanographic realizations
Bibliographic record
Abstract
In a seminal paper, Zhou et al. [J. Acoust. Soc. Am. 90, 2042–2054 (1991)] introduced the concept of large anomalous signal loss due to a resonance effect caused by solitons (internal waves traveling along the thermocline). They assumed that solitons were present and produced a remarkable comparison between acoustic model predictions and their acoustic measurements taken over a four-year period. They did not take sufficient oceanographic measurements to confirm the existence of solitons. Numerous investigators have tried to duplicate their pioneering work in similar shallow-water environments with solitons present. Signal reduction, mode conversions, and resonance-like effects have been observed, but signal loss of the magnitude measured by Zhou et al. have not been observed. We have used a primitive equation soliton model and the tidal flow near the Shandong peninsula to generate soliton simulations that flow into the Yellow Sea near the region where Zhou et al. made their acoustic measurements. We are performing analysis similar to that of Zhou et al. on these soliton realizations to determine if large signal losses can occur. Results from this investigation will be presented and discussed. [Work supported by ONR/NRL and by a High Performance Computing DoD grant.]
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".