Modeling Artificial Aeration Kinetics in Ice-Covered Lakes
Why this work is in the frame
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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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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) | 0.001 | 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 it