Geoacoustic uncertainties in matched-field inversions of ship-noise data
Bibliographic record
Abstract
This paper applies geoacoustic inversion to ship-noise data recorded on a horizontal array deployed on the seafloor in an experiment conducted by FFI in the Barents Sea. A Bayesian matched-field inversion method is employed which quantifies geoacoustic uncertainties and allows for quantitative comparisons of results from different data sets. Estimates of sediment parameters and their uncertainties were obtained by inversions of low-frequency narrowband data from a quiet research ship at source-array ranges of 1-6 km and different orientations with respect to the array, as well as from a merchant ship at ranges of 6-8 km. Multiple, independent data segments are included in the inversion and shown to significantly reduce geoacoustic parameter uncertainties. The effect of the state of prior information on the ship track (e.g., well known or unknown) on geoacoustic information content is also examined. Estimates of the sound-speed profile and density of sediment are compared with results from inversion of data from a towed controlled-source, and with reference values from other geophysical data collected in the area.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".