Comparison of focalization and marginalization for Bayesian tracking in an uncertain ocean environment
Bibliographic record
Abstract
This paper compares focalization and marginalization approaches to source tracking when uncertain ocean environmental parameters are included, in addition to source locations, in a Bayesian inversion formulation. Focalization consists of determining the source track that maximizes the posterior probability density (PPD) over all source and environmental parameters. An efficient focalization approach is developed by applying the Viterbi algorithm to compute the optimal track from range-depth conditional probability distributions for each realization of the environmental parameters. This allows source locations to be treated implicitly and the optimization to be applied only to environmental parameters, substantially reducing the dimensionality and complexity of the problem. Marginalization consists of first integrating the PPD over the environmental unknowns to obtain a sequence of joint marginal probability distributions over source range and depth along the track. Applying the Viterbi algorithm to these marginal distributions defines the track estimate, and the distributions themselves quantify the track uncertainty. Monte Carlo analysis of the two approaches for a test case involving both geoacoustic and water-column uncertainties indicates that marginalization provides a significantly more reliable approach to tracking in an unknown environment.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".