Assessing efforts to mitigate the impacts of drainage on wetlands in Ontario, Canada
Bibliographic record
Abstract
The protection of wetlands through the Ontario Drainage Act has been the subject of much debate. While seen as essential for increasing production and/or productivity of agricultural areas, drainage schemes have been usually approved at the expense of wetlands. Despite the presence of a referral process in Ontario's Drainage Act that is supposed to prevent the significant loss of wetland area, incremental losses continue to occur. The referral process allows landowners, drainage engineers, Drainage Superintendents, local conservation authorities and Ontario Ministry of Natural Resource officials to participate in the decision‐making process. This research examines the recommended mitigation measures and wetland gains/losses in Zorra Township between 1978 and 1997. Data sources included drainage files, wetland evaluation files, aerial photography and interviews with government officials. The results indicate that while recommended mitigation measures of drainage schemes in the vicinity of wetlands have increased, incremental losses continue to occur. The negotiated settlements among drainage engineers and the referral agencies appear to be inadequate to maintain the spatial extent of wetlands. The regulatory approach fails to motivate changes in land‐use management practices. This supports the need to include nonregulatory incentives in the effort to protect wetlands.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".