Ocean acoustic backscattering: When you can ignore acoustic scatter from turbulence and when you can’t
Bibliographic record
Abstract
While models predicting measurable levels of acoustic backscattering from oceanic turbulence have been around for decades, they have proven notoriously hard to confirm. This is, in part, because potential turbulent scattering layers often coincide with zooplankton layers. Therefore, as zooplankton are known to cause measurable acoustic scatter, the source of scatter is obscured. Furthermore, estimates of zooplankton abundance from acoustic scattering measurements have been shown to agree with independent measures under a number of circumstances. This gives circumstantial evidence to fuel the belief that scatter from turbulence is negligible. In addition, even if the turbulent scattering theory is correct, it predicts that over most of the ocean the turbulent intensities and/or stratifications are too weak to give turbulent scatter of a similar magnitude as is observed from zooplankton layers. Yet, despite all this, here data are presented that show circumstances when scatter from turbulence is significant. The areas of the ocean (mostly coastal) that one might have to be concerned about turbulent scatter are discussed, as well as the intriguing idea of using technology already developed for zooplankton observation to measure turbulence. This technology could facilitate the in situ study of biophysical interactions between zooplankton and turbulence.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".