A comparison of simulated and measured lake ice thickness using a Shallow Water Ice Profiler
Bibliographic record
Abstract
Abstract In northern regions where observational data is sparse, lake ice models are ideal tools as they can provide valuable information on ice cover regimes. The Canadian Lake Ice Model was used to simulate ice cover for a lake near Churchill, Manitoba, Canada throughout the 2008/2009 and 2009/2010 ice covered seasons. To validate and improve the model results, in situ measurements of the ice cover through both seasons were obtained using an upward‐looking sonar device Shallow Water Ice Profiler (SWIP) installed on the bottom of the lake. The SWIP identified the ice‐on/off dates as well as collected ice thickness measurements. In addition, a digital camera was installed on shore to capture images of the ice cover through the seasons and field measurements were obtained of snow depth on the ice, and both the thickness of snow ice (if present) and total ice cover. Altering the amounts of snow cover on the ice surface to represent potential snow redistribution affected simulated freeze‐up dates by a maximum of 22 days and break‐up dates by a maximum of 12 days, highlighting the importance of accurately representing the snowpack for lake ice modelling. The late season ice thickness tended to be under estimated by the simulations with break‐up occurring too early, however, the evolution of the ice cover was simulated to fall between the range of the full snow and no snow scenario, with the thickness being dependant on the amount of snow cover on the ice surface. Copyright © 2011 John Wiley & Sons, Ltd.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".