Probing the integration of land use and watershed planning in a shifting governance regime
Bibliographic record
Abstract
Effective governance that contributes to the integration of water management and land use planning is essential for successful protection of drinking water sources and, ultimately, provision of safe drinking water. In many jurisdictions, land use planning and watershed management occur on separate tracks. This article examines the prospects for integration of these two critical processes. A multicase study approach is used, focusing on the specific objective of protection of drinking water sources. Experiences in three case study watershed regions in Ontario, Canada (Grand River, Upper Thames, and Lake Simcoe), were analyzed. The goal was to identify the extent to which source water protection components and indicators are expressed in land use and watershed‐based planning documents. Similarities and differences among the watershed regions are distinguished through a cross‐case analysis. The results suggest that a shifting governance regime for drinking water safety in Ontario is contributing to integration between land use and water management. However, proactive and ongoing efforts are required to ensure that integration occurs and that barriers to integration are addressed. Timely guidelines, incentive‐based tools, up‐to‐date and accurate information, and adequate financial resources are essential.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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 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".