Determining a geoacoustic model from shallow-water transmission loss data using parameter linkage and a hybrid inversion algorithm
Bibliographic record
Abstract
In order to characterize the propagation conditions along a shallow-water sound range at low frequencies, measurements have been made of both cw transmission loss versus distance, and travel times of airgun-generated head waves. The head wave data yield the sound speed and time intercept of a reflecting interface, and these results are used as known parameters when the cw data are inverted to obtain a complete geoacoustic model. The inversion algorithm was a hybrid of the simplex and simulated annealing methods, similar to versions developed recently at the University of Victoria, Canada. The geoacoustic model was assumed to consist of two uniform solid layers overlying a solid uniform basement. The sound speed of the upper layer was estimated from the measured seafloor grain size, in accordance with empirical data. To further reduce the number of parameters, regression equations were devised to relate the less critical parameters (density, shear speed, and shear absorption) to those that are usually found to be more important (sound speed, sound absorption, and layer thickness), although the basement shear speed was also found to be an important parameter. With only six unknown parameters, the inversion algorithm generally found a satisfactory geoacoustic model after only several hundred runs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".