Modelling Changes in Stream Water Quality Due to Climate Change in a Southern Ontario Watershed
Bibliographic record
Abstract
This research represents a pilot project to establish a methodology for assessing the sensitivity of watershed stream water quality to changes in water quantity caused by climate change. The pilot watershed is the Duffins Creek watershed, located 20 km east of the City of Toronto, Canada. Scenarios of climate change analyzed in this project were drawn from two internationally recognized climate models: the Canadian Centre for Climate Modelling and Analysis (CCCma) CGCM1 and the Hadley Centre HadCM2. The AGNPS (Agricultural Non-Point Source) model was used to predict changes in stream water chemistry. The results are compared to baseline conditions as well as future conditions based on 2020 land use scenarios. It was determined that 2020 land use scenarios typically result in much smaller changes in peak flows than are predicted for the climate change scenarios, especially the wet climate change scenarios. Understanding climate change responses is critical for the development of watershed plans and drinking water source protection studies. Currently, watershed studies are completed using climate information based on relatively short-term monitoring databases that reflect past weather patterns. It is widely understood that management actions advocated in watershed studies could be improved if consideration were given to the implication of climate changes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".