Remote sensing mixed layer depth using ocean ambient noise
Bibliographic record
Abstract
Ambient noise measurements have been widely used to estimate environmental information such as water column sound speed, pH, seabed properties, and wind speed. In this study, 30 days of ambient noise data recorded on two vertically oriented hydrophones deployed near Alvin canyon on the New England shelf break were used to estimate the ocean mixed layer depth (MLD). The vertical noise coherence was computed and compared to a wave-number integral noise model comprised of a two-segment piecewise linear summer sound speed profile in a shallow water waveguide. Measurements of noise and sound speed profiles, together with a wavenumber integral ambient noise model were used to calculate the mixed layer thickness. Noise model results showed variations in the first zero-crossing frequency, which was in accordance with the semi-diurnal variability of the MLD. MLD was determined by matching the zero-crossing frequency of the real part of measured coherence with the model results for the entire one-month period. The comparison of the estimated MLD using ambient noise showed good agreement with the measured MLD from the temperature sensors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".