The angle of intromission in muddy sediments: Measurements and inferences
Bibliographic record
Abstract
Fine-grained sediments, e.g., mud, often exhibit sound speeds less than that of the interstitial fluid. This leads to an angle of intromission (AI), or Brewster angle, at which there is nearly complete transmission of acoustic energy from the water column into the sediment. It is generally difficult to observe this angle directly inasmuch as (1) fine-grained sediments typically have a very low attenuation and (2) underlying granular sediments have a higher sound speed and thus the total seabed reflected field tends to be dominated by the underlying more highly reflective sediments. In some cases, the AI can be directly observed by time-gating the reflected field such that only the fine-grained sediments are insonified. Nominally, the AI is independent of frequency. However, reflection measurements from hundreds of hertz to several kilohertz show a frequency dependence. This dependence is due to sound speed and density gradients (which thus can be estimated from the reflection data). Reflection measurements and core data indicate that geoacoustic gradients depend upon location on the continental shelf. In the Italian littoral, density gradients are always observed to be positive and decrease seaward. Curiously, sound speed gradients can be positive or negative and have a less obvious correlation with position. [Research supported by ONR Ocean Acoustics.]
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".