Estimating geoacoustic parameters from broadband data from the New Jersey shelf
Bibliographic record
Abstract
This paper presents the geoacoustic inversion of broadband data to estimate the sea bed parameters of SWARM95 site. The data set used in this study contains air gun signals that were transmitted at the depths of 25 and 45 m, respectively, below the sea surface, while the source ship was maneuvering at small speed (towing); the signals then were collected at the ranges around 5 km by a vertical line array. A canonical geoacoustic model was generated first according to previous experimental research results, for the purpose of a parameter sensitivity study; then both hybrid optimization and Bayesian inversion techniques were applied to the real data. The data error covariance matrix is estimated to resolve the spatial correlated data error in Bayesian inversion approach. Since the ratio of range and water depth is rather large in this study, only the geoacoustic parameters at the top of the sediment are sensitive in the inversion. The optimization results show the consistency of sensitive geoacoustic/geometric parameter estimations over the ranges and different source depths. Bayesian inversion gives the estimation uncertainties of the geoacoustic properties by marginal probability distributions. [Work supported by ONR.]
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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 | 0.000 |
| 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".