Laboratory study of the correlation between frazil ice particle and floc properties
Bibliographic record
Abstract
Abstract Frazil ice particles form in turbulent supercooled water and frazil flocs form by aggregation of frazil particles in the turbulent flow through the process of flocculation. Frazil flocs eventually become buoyant enough that they rise to the surface becoming frazil ice pans, contributing to the surface ice generation and ice cover formation. In this study, a series of laboratory experiments were performed in a frazil ice tank to investigate the correlation between frazil particle and floc properties under different air temperatures and turbulent dissipation rates to further our understanding of the frazil flocculation process. A high-resolution camera system was used to capture time-series images of frazil particles and flocs between two cross-polarising filters. Precision temperature recorders were used to monitor water and air temperatures. Time series of frazil particle and floc properties were obtained to analyze their correlations. Results show a strong linear relationship between particle and floc number concentrations with a floc-to-particle number concentration ratio ranging from 0.29~0.35. The ratio was not affected much by changing air temperature but was reduced by 12~17% at a lower turbulence dissipation rate. A moderate to strong nonlinear correlation was found between mean particle size and mean floc size described by an exponential relationship when particle mean sizes increased or decreased significantly. When particle mean size reaches an approximate equilibrium, a weak to moderate linear correlation was found between mean particle and floc size and the negative slope suggests they are inversely correlated.
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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 | 0.000 | 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) | 0.000 | 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".