Bibliographic record
Abstract
Measurements of frazil ice characteristics in both laboratory and field environments have each been hindered by different challenges to date. In the laboratory, the resolution of the digital imaging systems used to photograph suspended particles has limited the size of the smallest frazil ice crystals that could be observed. In field settings there has not been a practical method by which to directly measure in-situ frazil ice particles due to the difficulty of capturing clear, underwater photographs in harsh winter conditions. As a result, most field studies to date have been carried out using acoustic devices to detect suspended particles. However, these measurements require direct observations for calibration and validation. This study was designed to overcome the challenges faced by previous studies in order to measure complete size distributions of frazil ice particles throughout the supercooling process at various turbulence intensities and in various rivers.A series of laboratory experiments were conducted in which frazil ice particles were produced at three different turbulence intensities. The water temperature was measured and high-resolution, cross-polarised digital images of suspended frazil crystals as small as 22 μm were captured throughout each experiment. An image processing algorithm was written to analyse the frazil ice images and calculate the moving average mean and standard deviation of the particle diameter, and the number of suspended particles throughout the supercooling process. The mean particle diameter was calculated to be 0.94, 0.66, and 0.59 mm with standard deviations of 0.73, 0.51, and 0.45 mm at turbulent kinetic energy (TKE) dissipation rates of 23.9, 85.5, and 336 cm2/s3, respectively. The mean particle size was observed to reach a maximum shortly after the maximum degree of supercooling was reached, then decrease and remain at a constant value during the residual supercooling phase. A lognormal distribution was a good fit to the particle size distribution at all stages of the supercooling process.A digital imaging system, called the FrazilCam, was designed and constructed for use in field environments. The FrazilCam was successfully deployed in the Kananaskis, Peace, and North Saskatchewan Rivers in Alberta. Images captured using the FrazilCam in the first deployment season in 2014-15 were analysed and it was discovered that suspended sediment particles with diameters on the order of 0.1 mm were visible in the images and indistinguishable from ice. This issue was overcome by training support vector machine (SVM) algorithms to identify the differences between sediment and ice particles in each river. The SVM algorithms were able to classify sediment particles with 98% accuracy and remove them from the frazil ice size distributions. Using the SVM algorithms, data from the 2014-15, 2015-16, and 2016-17 freeze-up seasons were analysed. The mean particle diameter was found to range from 0.63 to 1.32 mm during the principal supercooling phase, and from 0.32 to 0.93 mm during the residual supercooling phase. Additionally, the number concentration of suspended frazil crystals varied from 1.48 × 104 to 1.81 × 106 particles/m3. Assuming a constant particle aspect ratio of 37, the volume concentration was estimated to range from 1.0 to 18 × 10−6 m3/m3. Time-series data collected using the FrazilCam indicated that the mean particle diameter and concentration remain approximately constant throughout the residual supercooling phase, and a lognormal distribution was confirmed to describe all of the size distributions calculated under steady flow conditions. A unique supercooling event was recorded during one of the FrazilCam deployments in which the maximum degree of supercooling was −0.145°C. On this occasion ice predominantly grew as shard-like crystals on submerged objects including the bed material rather than suspended disc-shaped frazil crystals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".