Matched-field geoacoustic inversion with a horizontal array and low-level source
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper applies geoacoustic inversion to acoustic-field data collected on a bottom-moored horizontal line array due to a continuous-wave towed source at a shallow water site in the Barents Sea. The source transmitted tones in the frequency band of 30–160Hz at levels comparable to those of a merchant ship, with resulting signal-to-noise ratios of 9–15dB. Bayesian inversion is applied to cross-spectral density matrices formed by averaging spectra from a sequence of time-series segments (snapshots). Quantifying data errors, including measurement and theory errors, is an important component of Bayesian inversion. To date, data error estimation for snapshot-averaged data has assumed either that averaging reduces errors as if they were fully independent between snapshots, or that averaging does not reduce errors at all. This paper quantifies data errors assuming that averaging reduces measurement error (dominated by ambient noise) but does not reduce theory (modeling) error, providing a physically reasonable intermediary between the two assumptions. Inversion results in the form of marginal posterior probability distributions are compared for the different approaches to data error estimation, and for data collected at several source ranges and bearings. Geoacoustic parameter estimates are compared with data from supporting geophysical measurements and historical data from the region.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it