Bibliographic record
Abstract
Encoded in the spectral density, spatial variability, and directionality (spatial coherence) of the ambient sound field is information on the generation mechanisms of sound and the properties of the ocean propagation environment and its boundaries. Through field and observatory measurements, and analytical and computational models of the underwater sound field, a research program has been pursued that asks, “What can we learn about the ocean by listening?” Large acoustic data sets have been exploited to develop an estimate of the effective source level per unit area of surface generated noise. In complement, a methodology for precisely partitioning the sound field into ship generated and wind generated components by exploiting the vertical noise directionality has been demonstrated. Models of the spatial properties of wind driven and ship generated sound have further been used to estimate the geoacoustic properties of the seabed, the depth of mix layer, depth-averaged pH, and localize a source in bearing and azimuth using only a pair of vertically oriented omnidirectional hydrophones. An autonomous passive acoustic profiler, The Deep Acoustic Lander (DAL), recently made measurements of the ambient sound field from the surface to the bottom of the Challenger Deep, Mariana Trench, precisely determining the mixing of a locally- and distantly generated contributions to the sound field. Meanwhile, DAL measurements at the Endeavour hydrothermal vent field have revealed components of the sound field generated by vent activity.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".