On the Measurement of Ocean Acidity with Ambient Sound
Bibliographic record
Abstract
The volume-integrated pH of seawater can be determined from the frequency and depth dependence of wind-generated ambient noise in the ocean. Over the 1 − 10 kHz frequency band, three main processes contribute to the acoustic attenuation in seawater: the chemical relaxation of boric acid and magnesium carbonate (< 3 kHz, related to pH), and magnesium sulfate (> 3 kHz, unrelated to pH). When local winds are strong (> 10 m/s), the ambient noise is dominated by locally generated surface noise, which exhibits a depth-independent directionality, and weak frequency and depth-dependent intensity. By measuring the depth-dependence of the spectral slope, the pH may be estimated from a comparison of the experimental data with an analytical model of ambient noise. Measurements of the depth-dependent ambient noise field were carried out in the Philippine Sea, Mariana Trench, and Tonga Trench from 2009 to 2021. The wideband (5 Hz - 30 kHz) acoustic data were recorded with untethered, free-falling, autonomous instrument platforms known as Deep Sound, equipped with two or four hydrophones. In all the data collected, the power spectral slopes became steeper with depth due to the stronger attenuation of high frequencies compared to low frequencies. Depth-averaged pH values, ranging from 7.68 to 8.35, were obtained from eight instrument drops. The noise spectral method, which has the potential for determining the depth-averaged value of pH, with the averaging depth being adjustable, could be suitable for the long-term passive acoustic monitoring of ocean acidity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".