Informing Canada’s commitment to biodiversity conservation: A science-based framework to help guide protected areas designation through Target 1 and beyond
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| 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".