Wintertime Simulations of a Boreal Lake with the Canadian Small Lake Model
Bibliographic record
Abstract
Abstract A one-dimensional mixed layer dynamic lake model is enhanced with snow and ice physics for an examination of processes governing ice cover and phenology in a small boreal lake. The complete snowpack physics module of the Canadian Land Surface Scheme along with a new snow-ice parameterization have been added to the Canadian Small Lake Model, and detailed meteorological and temperature profile data have been acquired for the forcing and evaluation of two wintertime simulations. During the first winter, simulated ice-on and ice-off biases were −3 and −5 days, respectively. In the second winter simulation, ice-on bias was larger, likely due to the absence of a frazil ice scheme in the model, and simulated ice-off was 6 days late, evidently due to insufficient convective mixing beneath the ice in the weeks leading up to ice-off. Ice cover was simulated about 25% too thin between January and March for this year, though late January simulated snow and snow-ice amounts were close to observed. The impact of snow-ice production on simulated ice cover and phenology was found to be dramatic for this lake. In the absence of this process, January snow was more than twice as deep as observed and March ice thickness was less than one-third of that observed. Without snow-ice production, a reasonable simulation of ice cover could only be restored if 62% of snowfall was removed ad hoc (e.g., through blowing snow redistribution)—an excessive amount for a small, sheltered boreal lake.
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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 | 0.000 |
| 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 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".