Estimating the uncertainty of geoacoustic parameters of a range-dependent environment
Bibliographic record
Abstract
In this study, data from a range-dependent environment are inverted to obtain estimates of the geoacoustic properties and their uncertainties. The technique consists of combining the results of a series of range-independent inversions to produce a model of the range-dependent environment. A rigorous uncertainty analysis provides a way of discerning whether variations in the inversion results are due to range-dependent features of the environment or simply due to uncertainty/variability in the results. Broadband acoustic data from a track off the island of Sicily were analyzed. Ground-truth information in the form of core measurements and a high-resolution seismic profile were also collected and used for verification of the results. The method of fast Gibbs sampling (FGS) was used to estimate the uncertainties of the geoacoustic properties. FGS is based on a Bayesian approach to inversion which samples the posterior probability distribution to estimate marginal probability distributions and parameter correlations. Marginal probability distributions were computed at various points along the track and compared to the ground-truth information. Overall, the analysis showed that the dominant range-dependent features of the environment could be estimated.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".