Anisotropy in high-frequency broadband acoustic backscattering in the presence of turbulent microstructure and zooplankton
Bibliographic record
Abstract
High-frequency broadband (120-600 kHz) acoustic backscattering measurements have been made in the vicinity of energetic internal waves. The transducers on the backscattering system could be adjusted so as to insonify the water-column either vertically or horizontally. The broadband capabilities of the system allowed spectral classification of the backscattering. The distribution of spectral shapes is significantly different for scattering measurements made with the transducers oriented horizontally versus vertically, indicating that scattering anisotropy is present. However, the scattering anisotropy could not be unequivocally explained by either turbulent microstructure or zooplankton, the two primary sources of scattering expected in internal waves. Daytime net samples indicate a predominance of short-aspect-ratio zooplankton. Using zooplankton acoustic scattering models, a preferential orientation of the observed zooplankton cannot explain the measured anisotropy. Yet model predictions of scattering from anisotropic turbulent microstructure, with inputs from coincident microstructure measurements, were not consistent with the observations. Possible explanations include bandwidth limitations that result in many spectra that cannot be unambiguously attributed to turbulence or zooplankton based on spectral shape. Extending the acoustic bandwidth to cover the range from 50 kHz to 2 MHz could help improve identification of the dominant sources of backscattering anisotropy.
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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 | 0.001 |
| 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".