Quantifying multifrequency acoustic characterization accuracy for icemodel development applications
Bibliographic record
Abstract
Abstract. Multi-frequency acoustic profiling is critically examined to estimate accuracies currently attainable in characterizing frazil suspensions: with primary interests focused on measuring fractional ice volume, a key factor in river ice growth models. The central issue is the adequacy of representations of backscatter cross sections of disk shaped frazil particles in a well-established theory of elastic spherical targets. An initial investigation established criteria for the existence of three-frequency solutions capable of providing lognormal statistical descriptions in terms of effective radii. These criteria restricted analyses of available field data with such models to inputs at two-frequencies limiting outputs to: a common effective radius, particle number density and frazil volume. Additional frazil cross section information is shown to be required to more fully exploit the full capability of multi-frequency profiling. An approximate relationship between cross sections and the product of acoustic wavenumbers and particle effective radii (k1ae) is developed from laboratory polystyrene disk and sphere data and transformed into the natural ice environment. Field data within the transformed range is transposed to higher frequencies in order to allow testing at still larger field values of k1ae. Two-frequency analyses utilizing the resulting “pseudo-frazil” relationship confirmed a close match with the field data and increased compatibility with existence criteria for three-frequency solutions. The results showed that, within transducer calibration limits, the originally tested spherical backscattering extractions consistently under-estimate frazil ice volume concentrations by 25 % confirming its continued use for accurate estimates in conjunction with a constant scaling factor of about 1.25.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".