Protecting biodiversity in British Columbia: Recommendations for developing species at risk legislation
Bibliographic record
Abstract
British Columbia has the greatest biological diversity of any province or territory in Canada. Yet increasing numbers of species in British Columbia are threatened with extinction. The current patchwork of provincial laws and regulations has not effectively prevented species declines. Recently, the Provincial Government has committed to enacting an endangered species law. Drawing upon our scientific and legal expertise, we offer recommendations for key features of endangered species legislation that build upon strengths and avoid weaknesses observed elsewhere. We recommend striking an independent Oversight Committee to provide recommendations about listing species, organize Recovery Teams, and monitor the efficacy of actions taken. Recovery Teams would evaluate and prioritize potential actions for individual species or groups of species that face common threats or live in a common area, based on best available evidence (including natural and social science and Indigenous Knowledge). Our recommendations focus on implementing an adaptive approach, with ongoing and transparent monitoring and reporting, to reduce delays between determining when a species is at risk and taking effective actions to save it. We urge lawmakers to include this strong evidentiary basis for species recovery as they tackle the scientific and socioeconomic challenges of building an effective species at risk Act.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".