Élaboration d’un cadre d’évaluation de l’efficacité des politiques de protection des milieux humides
Bibliographic record
Abstract
The protection of wetlands has been a global environmental concern for several decades. This is reflected in the adoption of the no net loss objective and the application of the ‘avoid-reduce-compensate’ mitigation sequence in many jurisdictions. However, government policies and programs have been found to be deficient in operationalizing avoidance and reducing impacts. The compensation option is often considered a priority over impact avoidance and reduction, hindering the achievement of no net loss. In this article, we develop an assessment framework to predict the effectiveness of government measures put in place to achieve the goal of no net loss of wetlands. This framework includes eight main criteria (net gains and specific targets; limits to compensation; mitigation sequence; equivalence between losses and gains; intermediate losses; proximity; stakeholder integration; monitoring and enforcement) and sub-criteria derived from scientific and gray literature. We then tested this framework by applying it to three jurisdictions (Quebec, Ontario, and Vermont) to validate its accuracy and relevance. We note that protection measures in the three jurisdictions studied have several shortcomings that will prevent achieving no net loss, whereas current scientific findings indicate that net gains should be the goal.
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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 | 0.042 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".