Informing Canada’s commitment to biodiversity conservation: A science-based framework to help guide protected areas designation through Target 1 and beyond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biodiversity is intrinsically linked to the health of our planet—and its people. Yet, increasingly, human activities are causing the extinction of species, degrading ecosystems, and reducing nature’s resilience to climate change and other threats. As a signatory to the Convention on Biological Diversity, Canada has a legal responsibility to protect 17% of land and freshwater by 2020. Currently, Canada has protected ∼10% of its terrestrial lands, requiring a marked increase in the pace and focus of protection over the next three years. Given the distribution, extent, and geography of Canada’s current protected areas, systematic conservation planning would provide decision-makers with a ranking of the potential for new protected area sites to stem biodiversity loss and preserve functioning ecosystems. Here, we identify five key principles for identifying lands that are likely to make the greatest contribution to reversing biodiversity declines and ensuring biodiversity persistence into the future. We identify current gaps and integrate principles of protecting ( i) species at risk, ( ii) representative ecosystems, ( iii) intact wilderness, ( iv) connectivity, and ( v) climate refugia. This spatially explicit assessment is intended as an ecological foundation that, when integrated with social, economic and governance considerations, would support evidence-based protected area decision-making in Canada.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it