Optimizing Artificial Aeration for Lake Winterkill Prevention
Bibliographic record
Abstract
Optimizing winter lake aeration equipment has never been quantified in situ with regard to air or water flow, polynya size (the open water area created by the aerators), or energy required to maintain adequate dissolved oxygen (DO) concentrations. We conducted experiments using different combinations of compressors, air diffusers and mechanical surface aerators in winterkill lakes in northwest Alberta in order to determine a simplified approach to aeration equipment sizing. A hyperbolic relationship existed between energy use and polynya size. The largest polynya sizes were created using 0.15 kW ha−1 with both submersed air injection and surface aerators. However, adequate DO concentrations were maintained with surface aeration using one-third to one-half of the energy used for air injection. Optimal sizing occurred with 0.15–0.23 kW ha−1 for air injection and 0.06–0.1 kW ha−1 for surface aeration.
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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 | 0.000 |
| 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 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".