Quantifying and transferring environmental uncertainties in underwater acoustic modeling
Bibliographic record
Abstract
Uncertainties in environmental inputs represent a major source of uncertainty in underwater acoustic model outputs and applications thereof (e.g., transmission-loss estimation, source localization). Seabed geoacoustic parameters are often estimated by the inversion of ocean acoustic data. Hence, rigorous quantification of geoacoustic inversion uncertainties and the transfer of these uncertainties to modeling applications are of key importance. Uncertainty estimation in geoacoustic inversion is naturally accommodated in a Bayesian formulation, which combines data and prior information to form the posterior probability density (PPD) of seabed parameters. Important components of this approach include quantitative model selection for seabed parameterizations consistent with the information content of the data; an appropriate model for residual data errors that specifies the likelihood function; and nonlinear estimation of the PPD, which is normally carried out using Markov-chain Monte Carlo (MCMC) methods. MCMC characterizes the PPD using a large ensemble of dependent random samples of the geoacoustic parameters, which can be computationally demanding. However, these uncertainties can be transferred efficiently to subsequent propagation-modeling applications using a much-smaller, randomly chosen (independent) subset from the ensemble. The approach is illustrated using simulations and inversion of ship noise recorded on a horizontal array of hydrophones at the New England Mud Patch.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".