The spatial properties of breaking wave generated and bedload transport generated noise in the sediment layer of a shallow water wave guide.
Bibliographic record
Abstract
In May of 2012, three weeks of ambient noise measurements from a hydrophone buried 30 cm deep in the sediment were recorded at Advocate Beach, a 1:10 sloped beach at the head of the Bay of Fundy, Nova Scotia. While tides varied the mean water depth between 0 and 4 m, 0.8 m surface waves passed overhead, driving sediment bedload transport and creating an ambient noise field in the sediment consisting of two primary components: noise generated by bubbles formed in breaking waves at the surface and noise generated by the collisions of sand, gravel and cobble in the bedload transport along the seabed. Both of these noise sources are stochastic and can be described by their second order statistics: power spectral density, spatial coherence and directional density. In an effort to distinguish these two noise sources, the spatial properties of three full wave models of the noise field in the sediment are compared, using an infinite sheet of sources placed near the surface of a Pekeris waveguide to model breaking wave noise, near the fluid-fluid interface of a Pekeris waveguide to model bedload transport noise, or near the fluid-fluid interface of two infinite half-spaces to model bedload transport noise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".