Optimal source tracking in an unknown ocean environment
Bibliographic record
Abstract
This paper develops and compares two approaches to localizing and tracking a low-level acoustic source when ocean environmental properties are unknown. Both approaches are based on a Bayesian formulation in which source and environmental parameters are considered random variables constrained by noisy acoustic data and by prior information on parameter values (e.g., physical limits for water-column and seabed properties) and on inter-parameter relationships (e.g., limits on horizontal and vertical source speed). One approach is based on determining the source track and environmental parameters that maximize the posterior probability density (PPD) using adaptive hybrid optimization. A key to solving this challenging problem efficiently is that the Viterbi algorithm is applied to compute the highest-probability source track for each environmental realization considered in the optimization-this provides the optimal track while requiring the optimization is applied only to environmental parameters. The second approach involves integrating the PPD over unknown environmental parameters to represent source-track information as a series of joint marginal probability surfaces over range and depth. The Viterbi algorithm is applied to extract the optimal track from these surfaces. For realistic environmental models (e.g., more than a dozen unknown parameters), the integration is carried out using efficient Markov-chain Monte Carlo methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".