Frazil ice measurements using the four-frequency AQUAscat sonar in laboratory and field environments
Bibliographic record
Abstract
This research describes laboratory experiments conducted to investigate the capability of a multifrequency Aquatec AQUAscat 1000R sonar in detecting and measuring frazil ice particles. A series of laboratory experiments were conducted at the University of Manitoba and the University of Alberta to measure the particle size and concentration of frazil ice particles using four transducers, including 0.3, 0.5, 2 and 4 MHz. Also, the AQUAscat Toolkit software was utilized to post-process the logged data using the sonar instrument. The results indicated that the 2 MHz transducer was the most sensitive to the presence of frazil ice particles, while the 0.3 MHz had the least sensitivity. The device started to detect the frazil ice particles when the maximum supercooling occurred. The concentration was determined to reach its maximum value of 0.65% and 0.45% in different setups at the University of Manitoba and the University of Alberta, respectively. The outcomes showed that the mean frazil particle size ranged from 100 to 300 µm at the University of Alberta in a supercooling event. In contrast, the findings based on experiments conducted at the University of Manitoba were unreliable due to the large number of bad cells within the particle size data. \nThe multifrequency sonar instrument was deployed on the riverbed at Dauphin River for a one-day experiment on December 6, 2022. It was revealed that the apparatus was able to detect the frazil ice particles but not the frazil flocs and ice rafts. The results showed that the average concentration of frazil ice was 0.0066%. Although the particle size outcomes were not reliable in the first 25 cm above the transducers, the average particle size in the rest of the water column was found to be between 150 and 300 µm.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".