A history of the study of low frequency sound absorption in the sea—An active 50 years and it isn't over yet
Bibliographic record
Abstract
In 1965, Thorp published a compilation of low frequency data from SOFAR propagation that indicated absorption below 10,000 Hz was greater than predicted. It was suggested this may be an artifact, but the data were shown to fit a 1 kHz relaxation mechanism. NUWC (formerly NUSC) began an extensive at-sea measurement program, ranging from Hudson Bay (jointly with Canadian DREP) to Lake Tanganyika, while several other laboratories obtained data in other oceans. Yeager and Fisher, using the T-jump method at the Case-Western Laboratory, determined there was a 1 kHz relaxation in seawater associated with boron. The data collected at-sea showed, however, a variation between ocean areas, notably values from the North Pacific were 1/2 those in the North Atlantic. This puzzled supporters and encouraged critics. At Scripps, Simmons and Fisher, using a resonating sphere, showed that the boron relaxation did indeed cause the absorption of low frequency sound. Browning and Mellen discovered the key to the at-sea variation was pH, and at NUWC Mellen confirmed this dependence in the laboratory using Simmons' sphere. This direct connection between low frequency absorption and ocean acidification has produced several recent papers on possible implications, and increasing global warming suggests an interesting future.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".