Bibliographic record
Abstract
Ocean-acoustic localization can be considered an inverse problem that involves estimating model parameters that specify the location of one or more acoustic sources and/or receivers based on fitting measured acoustic data, which can include observable quantities such as travel times, travel-time differences, modal dispersion, or acoustic-field structure. In a Bayesian inversion approach, data and prior information are used to compute the posterior probability density (PPD) of the model parameters, providing uncertainty analysis that quantifies the information content of the problem. The Bayesian formulation provides the generality to treat all uncertain parameters (e.g., both source and receiver locations, environmental properties, clock drifts) as unknowns subject to appropriate levels of prior information. Marginalizing over nuisance parameters can improve localization accuracy or at least account for parameter uncertainties in the localization uncertainty. Some Bayesian localization problems can be solved efficiently using linearization and iteration, with closed-form approximations for the PPD. In other cases, nonlinear (numerical) methods such as Markov-chain Monte Carlo sampling or trans-dimensional inversion are required. This talk will illustrate these concepts with a series of examples including array-element localization for moored and towed arrays, tracking autonomous underwater vehicles at a test range, marine-mammal localization, and multi-source matched-field localization
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".