Simulation of Ice Phenology on a Large Lake in the Mackenzie River Basin (1960–2000)
Bibliographic record
Abstract
ABSTRACT: A one-dimensional thermodynamic lake ice model (the Canadian Lake Ice Model or CLIMo) is presented and validated against in situ observations from Back Bay on Great Slave Lake (GSL) in the Mackenzie River basin, N.W.T. CLIMo results are also compared with freeze-up and break-up dates derived from SSM/I 85 GHz passive microwave data over GSL. Simulations are carried out over a 41-year period (1960–2000). Meteorological variables used as input for model simulations consist of air temperature, relative humidity, wind speed, cloud cover and snow on the ground. In addition, snow density derived from snow course measurements is used whenever available. The model output contains several information, notably ice and snow thickness as well as freeze-up and break-up dates. CLIMo reproduces lake ice phenology very well. Results show an excellent agreement between observed and simulated ice thickness and on-ice snow depth. The freeze-up and break-up dates are also well reproduced with a RMSE of 6 and 4 days, respectively. Undoubtedly, given the huge area of GSL, meteorological data from the Yellowknife airport weather station are probably representative of only a limited section of the lake. This is one of the reasons why a future investigation will focus on several weather stations around the lake.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".