Resolving meso-scale seabed variability using reflection measurements from an autonomous underwater vehicle
Bibliographic record
Abstract
Seabed geoacoustic variability is driven by geological processes that occur over a wide spectrum of space-time scales. While the acoustics community has some understanding of horizontal fine-scale geoacoustic variability, less than O(10(0)) m, and large-scale variability, greater than O(10(3)) m, there is a paucity of data resolving the geoacoustic meso-scale O(10(0)-10(3)) m. Measurements of the meso-scale along an ostensibly "benign" portion of the outer shelf reveal three classes of variability. The first class was expected and is due to horizontal variability of layer thicknesses: this was the only class that could be directly tied to seismic reflection data. The second class is due to rapid changes in layer properties and/or boundaries, occurring over scales of meters to hundreds of meters. The third class was observed as rapid variations of the angle/frequency dependent reflection coefficient within a single observation and is suggestive of variability at scales of meter or less. Though generally assumed to be negligible in acoustic modeling, the second and third classes are indicative of strong horizontal geoacoustic variability within a given layer. The observations give early insight into possible effects of horizontal geoacoustic variability on long-range acoustic propagation and reverberation.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".