Modeling the Impacts of Atmospheric Deposition on Water Quality in Lake Ontario Under Climate Change Scenarios
Why this work is in the frame
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Bibliographic record
Abstract
Water quality in urban areas in Canada is a major issue despite the fact that it has excessive resources of freshwater. Current methods of addressing the impacts of atmospheric deposition and climate change on water quality are inadequate. Physical methods are too complex and usually ignore the impacts of atmospheric deposition. Therefore, in this research two categories of data driven models have been developed using artificial neural networks to model the atmospheric deposition and water quality. These models were developed in three regions near Lake Ontario: Toronto, Cobourg, and Grimsby regions which have different characteristics of population and air contamination. The results showed in future, the atmospheric deposition contamination in summers and autumns will become higher than the present situation. However, the precipitation contamination in winters will be lower. Moreover, the atmospheric deposition can not influence the water quality of Lake Ontario considerably.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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