Representative reserve design in Canada: the contribution of existing protected areas
Bibliographic record
Abstract
Given the limited resources to set aside protected areas for biodiversity conservation, as well as competing land use interests, it is prudent that networks of protected areas represent biodiversity effectively and efficiently.Effective networks represent all species in protected areas that are large enough to ensure species persistence.However, such a network may not be efficient in terms of the amount of land allocated for conservation.Reserve selection algorithms are tools that can be used to delineate optimal (or nearoptimal) solutions to the problem of maximizing representation of species with a minimum amount of area.In this paper, I show how reserve selection algorithms can be used to determine whether existing protected areas that meet criteria for minimum reserve area are efficient and/or effective in terms of representing disturbance sensitive mammals in Canada.In general, existing protected areas do not effectively capture the full suite of mammalian biodiversity, nor are most existing protected areas part of a near-optimal solution set.The results of this analysis can help to identify targets for protected areas, and suggest priorities for establishment of new protected areas, or expansion of existing ones.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".