Analysis of the surface energy budget during supercooling in rivers
Bibliographic record
Abstract
In northern rivers, heat loss from the water surface is the key driver of supercooling in rivers and the subsequent generation of river ice. The ability to estimate the different surface heat components is crucial to accurately model supercooling and the various ice formation processes. To calibrate these models, concurrent water temperature and local meteorological data are needed, which can be a challenging task. Therefore, it is important to understand the relative importance of the different heat components on supercooling of water. For this purpose, the properties of 190 supercooling events observed during the 2016–2017 season on two regulated rivers in Alberta, Canada were analyzed together with the calculated surface heat budget using weather data from local weather stations. Longwave radiation was found to be the dominant negative heat flux for 80.0% of all events. During supercooling events, the longwave radiation and sensible components had average values of −65.7 and −46.6 W/m2, respectively. The evaporative heat flux component was found to be negligible with an average value −4.52 W/m2. Sensible heat flux tended to be the dominant cooling heat flux when the air temperature was approximately -15 °C or colder. The shortwave radiation component was the dominant warming heat flux for 97.4% of all events with an average value 52.6 W/m2. The diurnal cycling of the net heat flux due to shortwave radiation was found to be the most significant factor in determining the start and end of supercooling events.
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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 | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 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".