Mid frequency shallow water fine-grained sediment attenuation measurements
Bibliographic record
Abstract
Attenuation is perhaps the most difficult sediment acoustic property to measure, but arguably one of the most important for predicting passive and active sonar performance. Measurement techniques can be separated into "direct" measurements (e.g., via sediment probes, sediment cores, and laboratory studies on "ideal" sediments) which are typically at high frequencies, O(10(4)-10(5)) Hz, and "indirect" measurements where attenuation is inferred from long-range propagation or reflection data, generally O(10(2)-10(3)) Hz. A frequency gap in measurements exists in the 600-4000 Hz band and also a general acknowledgement that much of the historical measurements on fine-grained sediments have been biased due to a non-negligible silt and sand component. A shallow water measurement technique using long range reverberation is critically explored. An approximate solution derived using energy flux theory shows that the reverberation is very sensitive to depth-integrated attenuation in a fine-grained sediment layer and separable from most other unknown geoacoustic parameters. Simulation using Bayesian methods confirms the theory. Reverberation measurements across a 10 m fine-grained sediment layer yield an attenuation of 0.009 dB/m/kHz with 95% confidence bounds of 0.006-0.013 dB/m/kHz. This is among the lowest values for sediment attenuation reported in shallow water.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".