A laboratory study of the impact of varying air-water heat flux on supercooling and frazil ice generation
Bibliographic record
Abstract
The formation and evolution of frazil ice during river freeze-up is a key source for anchor ice and surface ice formation, both of which significantly impact river hydrology. Anchor ice formation on the riverbed may lead to flooding and accumulations on water intake trash racks may completely block inflows to water treatment plants. Numerous previous laboratory studies have investigated frazil ice generation during classic supercooling events which occur when the upward air-water heat flux remains constant. However, frazil generation when the heat flux varies during a supercooling event, which occurs commonly in the field, has not been explored in laboratory studies. To investigate this phenomenon, a series of controlled laboratory experiments were conducted in which variations in the air-water heat flux were induced by controlling the air temperature during the experiments. Images of frazil particles and flocs were captured while the air temperature was increased or decreased by 10 °C at different times during supercooling events. Varying the heat flux during different supercooling phases led to different responses in the time series of water temperature and frazil ice properties. Increasing the heat flux raised the mean particle number concentration by 25–33 %. Decreasing the heat flux only produced a measurable effect when the change occurred early in the supercooling event, prior to significant ice formation, reducing mean particle and floc number concentrations by 10 and 22 %, respectively. Particle and floc production rates varied by approximately a factor of two when the heat flux was increased or decreased prior to significant ice formation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".