Remote sensing of sediment density and velocity gradients in the transition layer
Bibliographic record
Abstract
The geoacoustic properties of marine sediments, e.g., bulk density and compressional velocity, commonly exhibit large variations in depth near the water-sediment interface. This layer, termed the transition layer, is typically of 0(10(-1)-10(0)) m in thickness. Depth variations within the transition layer may have important implications for understanding and modeling acoustic interaction with the seabed, including propagation and reverberation. In addition, the variations may contain significant clues about the underlying depositional or erosional processes. Characteristics of the transition layer can be measured directly (e.g., coring) or remotely. Remote measurements have the advantage of sampling without disturbing the sediment properties; they also have the potential to be orders of magnitude faster and less expensive than direct methods. It is shown that broadband seabed reflection data can be exploited to remotely obtain the depth dependent density and velocity profiles in the transition layer to high accuracy. A Bayesian inversion approach, which accounts for correlated data errors, provides estimates and uncertainties for the geoacoustic properties. These properties agree with direct (i.e., core) measurements within the uncertainty estimates.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".