Three‐dimensional simulation of lake and ice dynamics during winter
Bibliographic record
Abstract
An ice‐formation algorithm is implemented in the three‐dimensional Estuary and Lake Computer Model, to allow simulation of hydrodynamics and the thermal structure beneath the ice during winter. The one‐dimensional governing equation of heat conduction among the three layers of white ice, blue ice, and snow is solved for the formation of ice cover considering the heat flux through air and water. This algorithm is applied independently in each grid cell within the simulation domain, allowing for spatially variable ice formation. The model was validated against observed data from both a large and a small Canadian mid‐latitude lake (Lake Ontario and Harmon Lake, respectively). The lake surface temperature and the distribution and thickness of ice cover on Lake Ontario were predicted successfully during the 2006–2007 winter period. The model also accurately simulated spring 2007 temperature profiles, as typically used for the initial conditions for a summer simulation. The variation of ice and snow thickness, and vertical temperature profiles, were well‐simulated for Harmon Lake during winter 1990–1991. These comparisons demonstrate the applicability of the model for year‐round simulation of mid‐latitude lakes of varying size.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".