Bibliographic record
Abstract
This paper presents an overview of a general Bayesian inference approach to source localization, tracking and/or environmental estimation. Source location and spectral parameters together with environmental parameters are all considered unknown random variables to be estimated from prior information and observed acoustic data. The relative level of prior information for various parameters differentiates applications of interest. For instance, controlled-source geoacoustic inversion typically involves large prior uncertainties for seabed parameters but small uncertainties for source locations, although some applications, such as inverting noise from ships-of-opportunity, may involve larger location uncertainties. Alternatively, source localization in an uncertain environment typically involves non-informative location priors and environmental priors that reflect available knowledge. Tracking applications include additional prior constraints on source speed. In all cases, the goal is to compute marginals of the posterior probability density for source and environmental parameters, quantifying the information content of the data and prior. This is typically carried out with Markov-chain Monte Carlo methods including Metropolis-Hastings sampling and/or Gibbs sampling, with various approaches applied to improve efficiency (e.g., principal-component sampling, parallel tempering) and generality (trans-dimensional inversion). Multiple-source localization minimizes the Bayesian information criterion to estimate the number of sources.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".