Estimation of subbottom geoacoustic properties from offshore airgun surveys in Atlantic Canada
Bibliographic record
Abstract
This paper estimates seabed geoacoustic profiles at 14 sites offshore of the eastern Canadian coast using sound levels from a single airgun. A 210 in3 airgun was operated along two track lines at each site and a calibrated seabed-mounted Autonomous Multichannel Acoustic Recorder equipped with a single omnidirectional hydrophone recorded the airgun sounds. High-resolution bathymetry was acquired along the tracks using a ship-mounted multibeam system and conductivity-temperature-depth profiles were measured at each site. Seabed geoacoustic profiles were estimated in a non-linear trans-dimensional Bayesian inversion using energy spectral density levels at six frequencies between 10 and 320 Hz as a function of range. The inversion estimated the unknown number of subbottom layers, geoacoustic properties (including compressional and shear wave speeds and attenuations, density, and layer thicknesses), airgun source levels, a range-correction factor, and error statistics. Data were fit with error standard deviations of 0.6–5.6 dB. Convergence of the posterior probability density function was not reached within the time limits of the study so parameter uncertainty was estimated using a linearized formulation of the inverse problem centered around the model that minimized the Bayesian Information Criterion.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".