Modeling Artificial Aeration Kinetics in Ice-Covered Lakes
Bibliographic record
Abstract
A lake hydrodynamic model has been enhanced to simulate ice cover and artificial aeration during ice cover periods. Artificial aeration using mechanical surface aerators (“splashers”) and point-source bubblers (“bubblers”) is examined. Applying the model to two lakes in Alberta, Canada, indicate the model's capacity to handle a range of lake conditions and aeration operations. The sediment bed is found to be an important source of both heat and biochemical oxygen demand to the water column, during both natural conditions and artificial mixing periods. The ice cover thickness is shown to be a function of snow weight and insulation effects. The effects of an opening in the ice cover are a net gain in dissolved oxygen and a net loss of heat. The design and placement of aerators in the lake, as well as their operation schedules, are shown to determine the volume of mixed water and aeration effectiveness. This model is suitable for designing lake aeration systems to prevent winterkill in subarctic 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".