One track, two experiments four years apart: On the repeatability of geoacoustic inversion on the New England Mud Patch
Bibliographic record
Abstract
Our current knowledge of the geoacoustic properties of the New England Mud Patch (NEMP) is mostly driven by data collected in 2017 as part of the Seabed Characterization Experiment (SBCEX17). In 2021, a modest geoacoustic inversion experiment was performed on the NEMP using a simple and low-cost pair of experimental assets: a “TOSSIT” passive acoustic mooring and an impulsive “RIUSS” (Rupture Induced, Underwater Sound Source). The TOSSIT/RIUSS data were collected on a track that was studied intensively during SBCEX17, but with fundamental differences in oceanographic conditions: a frontal intrusion was present at the experimental site in 2021, creating a strongly stratified sound speed profile (SSP) in the water column, while the water column was essentially iso-speed in 2017. The 2021 TOSSIT/RIUSS data are used to perform geoacoustic inversion using warping and Bayesian trans-dimensional methods. The geoacoustic properties estimated for the 2021 data compare favorably to results obtained with SBCEX17 data, even when the 2021 data are inverted jointly for water-column SSP and seabed parameters. This study demonstrates inversion repeatability on the NEMP using data sets collected years apart and under different (and potentially unknown) oceanographic conditions. [Work supported by the Office of Naval Research.]
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".