Listening to the acidity of the ocean: Inversion of passive deep sea acoustic data for pH
Bibliographic record
Abstract
Ocean acidification is an ongoing concern due to its impact on the marine ecosystem. The volume integrated pH of sea water can be determined from the depth-dependence of ambient sound, which depends on the acoustic absorption properties of seawater. For a wind-driven noise in the ocean over the band 1–10 kHz, two main contributions to sound attenuation are associated with the ionic relaxation of boric acid (<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 and has a depth-independent directionality and a weakly frequency and depth-dependent intensity, due to sound absorption. By measuring the attenuation of sound in a wide frequency band, it is possible to estimate pH by comparing the experimentally measured attenuation with an analytical theory of passive acoustic absorption spectroscopy. Measurements of the depth-dependent ambient sound field were carried out in the Philippine Sea, Mariana Trench, and Tonga Trench throughout 2009—2021. The wideband (5 Hz—30 kHz) acoustic data were recorded with untethered free-falling autonomous recording systems carrying two or four hydrophones.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".