The effects of lead agency, nongovernmental organizations, and recovery team membership on the identification of critical habitat for species at risk: insights from the Canadian experience
Bibliographic record
Abstract
Evaluation of legislation and procedures in place to help recover species at risk of extinction is an important component of conservation efforts. Despite its biological importance and key role in species protection and recovery legislation, identification of critical habitat is inconsistently applied. We analyzed data from 126 recovery strategies implemented under Canada’s nascent (2002) Species at Risk Act (SARA) to determine how lead agency, Federal Court rulings, and the proportion of independent team members influenced identification of critical habitat. Only 17% of strategies led by the Department of Fisheries and Oceans included critical habitat, compared with 63% of strategies led by Environment Canada, indicating that aquatic species at risk are much less likely to have critical habitat identified. A 50% increase in recovery strategies that identified critical habitat following precedent-setting court judgments suggests that legal action by nongovernmental organizations played a key role in the evolution of recovery policy for species at risk in Canada. The proportion of independent scientists on a recovery team was statistically unrelated to identification of critical habitat at a national scale, but case studies indicate that independent team members may play an important role in ensuring compliance and transparency during recovery planning.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".