Drought Contingency Planning and Implementation at the Local Level in Ontario
Bibliographic record
Abstract
Responsibilities for water management in Ontario are shared among the federal, provincial and local levels of government. Recently, the local level (which includes municipalities and conservation authorities) has been assigned significantly more responsibilities. For example, the provincial government’s Ontario Low Water Response plan (OLWR) assigns key responsibilities to municipalities and conservation authorities. However, it is not clear that all local level agencies are capable of assuming these greater responsibilities. This paper reports findings from a study that used the community capacity literature to evaluate the role of the local level in drought contingency planning and implementation in two Ontario watersheds. The Big Creek watershed is dominated by agriculture, while the upper Credit River watershed faces great pressure from urban development. Both watersheds are dependent upon groundwater and have experienced reduced water supply during recent drought episodes. Based on an investigation into the roles, responsibilities and communication patterns among government agencies and non-government organizations in each watershed, it was concluded that watershed communities do have the capacity to create and implement a drought contingency plan. However, they require considerable assistance from the provincial government, especially in terms of regulating water withdrawals during periods of drought.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".