Assessing a proponent-driven process for endangered species threat mitigation: Ontario’s <i>Endangered Species Act</i>, American Eel, and hydropower
Bibliographic record
Abstract
American Eel (Anguilla rostrata) were used as a case study to assess whether Ontario’s Endangered Species Act proponent-driven regulatory approach resulted in successful imperilled species management outcomes. American Eel observation databases and proponent-prepared mitigation plans and monitoring data were used to assess whether: ( i) facilities within the distribution range were registered, ( ii) effects monitoring protocols were adequate to evaluate adverse effects of facilities, ( iii) proponents implemented mitigation actions that followed best management practices (BMPs), and ( iv) effectiveness monitoring designs were adequate to evaluate effectiveness of mitigation actions. Less than half of the facilities (8 of 17) within the extant species range were registered. Few eels were observed at each facility, precluding proponents from effectively evaluating the facilities’ effects. Mitigation actions following BMPs were only implemented for eel out-migration at three facilities. Half of the registered facilities implemented effectiveness monitoring, but experimental designs did not follow best practices and standards. To improve this proponent-driven approach, regulators could reduce ambiguity in regulation language and provide clearer, quantitative requirements for facility registration, effects monitoring, mitigation actions, and effectiveness monitoring. Proponents could improve monitoring efforts to establish species occurrence and generate baseline data to measure facility effects and mitigation action effectiveness.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".