Offline Implementation and Evaluation of the Canadian Small Lake Model with the Canadian Land Surface Scheme over Western Canada
Bibliographic record
Abstract
Abstract The Canadian Small Lake Model (CSLM), version 2, was run with the Canadian Land Surface Scheme (CLASS), version 3.6.1, in an offline regional test over western Canada. Forcing data were derived from ERA-Interim and downscaled using the fifth-generation Canadian Regional Climate Model (CRCM5). The forcing precipitation field was adjusted using monthly data from the Canadian Gridded Temperature and Precipitation Anomalies (CANGRD) observation-based dataset. The modeled surface air temperature was evaluated against CANGRD data, the modeled albedo against MODIS data, and the modeled snow water equivalent against Canadian Meteorological Centre (CMC) and Global Snow Monitoring for Climate Research (GlobSnow) data. The lake simulation itself was evaluated using the Along Track Scanning Radiometer (ATSR) Reprocessing for Climate: Lake Surface Water Temperature and Ice Cover (ARC-Lake) dataset. Summer surface lake temperatures and the lake ice formation and breakup periods were well simulated, except for slight warm/cold summer/fall surface temperature biases, early ice breakup, and early ice formation, consistent with warm/cold biases in the climate simulation. Tests were carried out to investigate the sensitivity of the CSLM simulation to the default values assigned to the shortwave extinction coefficient and the average lake depth, and changing the former from 0.5 to 2.0 m−1 and the latter from 10.0 to 50.0 or 5.0 m had minimal effects on the simulation. Comparisons of the average annual variations of the simulated net shortwave radiation, turbulent fluxes, snowpack, and maximum and minimum daily surface temperatures between the land and the lake fractions for tundra, boreal, and southern regions showed patterns consistent with those expected. Finally, a test of the CSLM over the large resolved lakes in the model domain demonstrated a performance comparable to that for subgrid lakes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".