A brief history of the discovery of the low frequency sound absorption mechanism in seawater and its <i>p</i>H dependence.
Bibliographic record
Abstract
In the 1960s tests with a new surface ship sonar indicated that low-frequency (less than 10 000 Hz) propagation loss was greater than predicted by the existing MgSO4 absorption model. Propagation measurements in the SOFAR channel showed that the anomalous absorption was up to ten times greater than predicted and could be fitted by a 1-kHz relaxation. This was not without controversy at the time, but interest in this fundamental parameter spurred a major measurement effort by the U.S. Navy Underwater Sound Laboratory and other laboratories in the US, Canada, New Zealand, Europe, and Australia, resulting in at-sea measurements from Hudson Bay to the Tasman Sea. Yeager and Fisher conducted laboratory t-jump measurements that identified a boron based low-frequency relaxation mechanism in seawater. Simmons and Fisher followed with resonant sphere measurements to quantify the resulting acoustic absorption. A compilation of the at-sea results showed, somewhat surprisingly, a correlation with pH: the lower the pH, the lower the absorption. Mellen quantified the pH dependence by extensive laboratory resonator measurements. These results were confirmed by measurements conducted in China. The possible application of acoustically monitoring ocean pH was first suggested by Browning and Mellen in 1990.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".