On geoacoustic inversions, sediment acoustics, and deep ocean ambient noise
Bibliographic record
Abstract
During the period of 2005–2011, three topics in acoustical oceanography were actively investigated in the Buckingham Lab which combined field, lab, and theoretical work. Geoacoustic inversions in the low frequency (<500 Hz) band were carried out using the noise generated by a light aircraft off the coast of La Jolla, California, and during the MAKAI experiment in Kauai, Hawaii. The inversion relied on a comparison of data to an analytical model of a moving airborne source over a two-layer shallow water waveguide. In an effort to investigate the link between grain roughness and porosity to support the development of a theory of acoustic propagation in sediments, a statistical model for the two-dimensional shape of sand grains was developed from the digitized outlines of grains from a dozen locations, including deserts, beaches, and seabeds. Lastly, a family of autonomous free-falling ambient noise profilers known as Deep Sound, were designed, manufactured, and deployed, returning measurements of ambient noise from below the critical (or reciprocal) depth in the deep ocean. Field experiments were carried out in the San Diego trough, the Philippine Sea, the Tonga Trench, and the Sirena Deep and Challenger Deep in the Mariana Trench.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".