Bayesian tracking predictions in an uncertain ocean environment.
Bibliographic record
Abstract
This paper considers probabilistic prediction of the future locations of a moving acoustic source in the ocean based on past locations as determined by Bayesian source tracking in an uncertain environment. The Bayesian tracking approach considers both source and environmental parameters as unknown random variables constrained by noisy acoustic data and prior information and numerically integrates the posterior probability density (PPD) over the environmental parameters to obtain a time-ordered sequence of joint marginal probability surfaces over source range and depth. The integration is carried out using Markov-chain Monte Carlo sampling methods which provide a large collection of track realizations drawn from the PPD. Applying a probabilistic model for source motion to each of these realizations produces a sequence of source range-depth probability distributions for future times. These predictions account for both the uncertainty of the source-motion model and the uncertainty in the state of knowledge of past source locations, which is itself dependent on environmental uncertainty. Several source-motion models, which differ in the degree of confidence assigned to future predictions based on past locations, are considered for range-depth tracking and prediction using a vertical sensor array.
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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 | 0.002 | 0.015 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".