The upper ocean ambient sound field as a tool to address significant scientific and societal questions
Bibliographic record
Abstract
David Farmer’s keen interest in understanding the dynamics of the upper-ocean and air-sea interaction convinced him that studying and using the naturally occurring high-frequency oceanic sound field would give additional insight into these processes. Breaking surface waves are important to ocean dynamics and were believed to be a significant source of the observed sound field. However, direct measurements were lacking. In the mid-1980s and onwards, David Farmer and his students developed a range of new observational techniques and instrumentation which would significantly improve our understanding of the sound field itself and how it relates to parameters such as wind speed and wave conditions, and as a tool to understanding the more fundamental physical processes of the upper ocean. All this research resulted in significant progress in our understanding of wave breaking, air-sea interaction, air-sea gas transfer, and indirectly to upper-ocean bubble distributions and dynamics, ice generated sound, and the role of anthropogenic noise on marine fauna. Here, we review key outcomes of this research and discuss how several components of Farmer et al.’s work now is being used to address significant societal issues with regard to the impacts of increasing levels of man-made underwater noise on marine life.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".