Estimates of the prevalence of anomalous signal losses in the Yellow Sea derived from acoustic and oceanographic computer model simulations
Bibliographic record
Abstract
The results from collocated oceanographic and acoustic simulations in a region of the Yellow Sea near the Shandong peninsula have been presented [Chin-Bing et al., J. Acoust. Soc. Am. 108, 2577 (2000)]. In that work, the tidal flow near the peninsula was used to initialize a 2.5-dimensional ocean model [K. G. Lamb, J. Geophys. Res. 99, 843–864 (1994)] that subsequently generated internal solitary waves (solitons). The validity of these soliton simulations was established by matching satellite imagery taken over the region. Acoustic propagation simulations through this soliton field produced results similar to the anomalous signal loss measured by Zhou, Zhang, and Rogers [J. Acoust. Soc. Am. 90, 2042–2054 (1991)]. Analysis of the acoustic interactions with the solitons also confirmed the hypothesis that the loss mechanism involved acoustic mode coupling. Recently we have attempted to estimate the prevalence of these anomalous signal losses in this region. These estimates were made from simulating acoustic effects over an 80 hour space-time evolution of soliton packets. Examples will be presented that suggest the conditions necessary for anomalous signal loss may be more prevalent than previously thought. [Work supported by ONR/NRL and by a High Performance Computing DoD grant.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".