Bibliographic record
Abstract
Over the past 25 years, many methods have been developed for estimating parameters of geoacoustic models of the ocean bottom from acoustic field data. The methods involved model-based comparisons between experimental data and calculated fields or field quantities. Benchmarking workshops based on simulated field data confirmed the ability of the methods for inverting geoacoustic model parameters for range-independent and range-dependent ocean environments. Although there are many published examples of geoacoustic inversions in various different ocean environments, the methods have not been tested in an experimental benchmark. This paper describes the design of an experimental benchmark for geoacoustic inversion methods, and presents an analysis of the performance of the methods with experimental data. The benchmark involves application of different inversion methods to data spanning a frequency band from 50to 2500 Hz that were obtained at a single well-surveyed ocean bottom site. The performance metric involves comparisons of: estimated geoacoustic profiles against the ground truth information of the site; modal wavenumbers for the inverted models; and transmission loss calculations based on the estimated models against measured data. The analysis shows that the methods are capable of estimating realistic geoacoustic profiles, with sound speed the most reliable estimated model parameter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".