High-frequency broadband acoustic scattering for investigating double- diffusive convection
Bibliographic record
Abstract
Polar regions, with their supercooled and relatively fresh surface water, are highly susceptible to the diffusive regime of double-diffusive convection (DDDC). The fluxes associated with DDDC can play a significant role in their heat/buoyancy budgets. The use of high-frequency acoustics as a tool to map the extent and evolution of DDDC in the ocean is explored through a series of laboratory 200-300 kHz broadband acoustic backscattering measurements. Pulse compression signal processing allows centimeter-scale interface thicknesses to be rapidly and remotely measured, and the evolution, and ultimate merging, of multiple centimeter-scale interfaces to be observed. Combining the acoustically measured interface thicknesses with knowledge of the relatively-constant temperatures within the surrounding layers allows the estimation of DDDC fluxes. Thus, broadband acoustics offers a rapid and remote method to infer fluxes, without the need for time-consuming microstructure measurements, suggesting that this technique could benefit field studies of DDDC. Using simple models to extrapolate to the thermohaline steps typically found associated with DDDC in polar regions, diffusive-convection interfaces are predicted to scatter at measurable levels in many areas. Narrowband (120 and 200 kHz) acoustic backscattering observations of two scattering layers in the Western Antarctic Peninsula support this prediction.
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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 | 0.001 |
| 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) | 0.001 | 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".