Science‐informed policy decisions lead to the creation of a protected area for a wide‐ranging species at risk
Bibliographic record
Abstract
Abstract Protected areas are needed to conserve nature and biodiversity worldwide. The province of Québec (Canada) recently established a large wilderness area affording significant habitat protection for boreal woodland caribou ( Rangifer tarandus caribou ), a wide‐ranging species at risk. We describe a decision support framework combining ecological modeling with socioeconomic constraints that ultimately led to the creation of this protected area. Multiple criteria were used to identify candidate protected areas for boreal caribou. These had to be large in size (>10,000 km 2 ) and located in regions where available high‐quality habitat was threatened by development pressures. Candidate areas also had to contribute substantively to the maintenance of functional habitat connectivity, be exempt from major industrial developments and recent fires, and required evidence of recent use by caribou. Five candidate protected areas emerged from this exercise. Key regional stakeholders were consulted, thereby strengthening advocacy for land designation, and boundaries were refined through their input, which helped further reduce socioeconomic conflicts. This process involved difficult compromises, but eventually led to the legal designation on March 4, 2021 of a new protected area for boreal caribou known as the Caribous‐Forestiers‐de‐Manouane‐Manicouagan. We show how our science‐informed decision support framework was instrumental in the success of this endeavor, and describe the obstacles overcame in the process, so that other jurisdictions may draw from this experience in their efforts to achieve similar conservation goals.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| 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".