Quantifying suspended frazil ice using multi‐frequency underwater acoustic devices
Bibliographic record
Abstract
Abstract An intensive frazil ice field sampling campaign was undertaken at the Port of Quebec on the St. Lawrence River from February–March 2009. Two underwater acoustic instruments set at different frequencies of 420 and 1228.8 kHz were used to detect frazil ice in the water column. In this paper, frequency inversion methods are presented and subsequently applied to the observations to estimate frazil ice characteristics and concentration. Using inversion methods, most of the detected ice crystals had estimated radii of 0.06–0.18 mm. This range compares favourably to the estimated value of 0.20 mm obtained by analysing the Rouse number related to the vertical distribution of the frazil crystals. The results were in contrast to a previous study of frazil ice at another site in the St. Lawrence, which reported radii tens of times larger . The tiny crystals observed here were of similar size to those observed in laboratories (e.g. 0.09 mm), suggesting that the particles formed locally. Frequency analyses were also used to estimate the volumetric suspended frazil concentration, which appeared to be on the order of 6 ppm. Based on evidence suggested by the data and the volume backscattering coefficient at the two frequencies, this study also presents the complex sequence of processes that occurs during a typical supercooling frazil event. This paper concludes with future directions for research using acoustic instrumentation for further understanding of frazil ice dynamics. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 | 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".