Bibliographic record
Abstract
Few studies have addressed patterns and causes of species endangerment at different resolutions and geographical extents. Using newly developed remote sensing and species distribution data sets, we examined the influence of both natural and anthropogenic factors on the density of terrestrial endangered species in Canada at two spatial scales. The first was at a national extent and the second was within a region of Canada (the mixed wood plains) where there are particularly large numbers of endangered species. We also examined the distribution of protected areas throughout Canada to determine their capacity to shelter endangered species. Land use, which is measured by 1‐km resolution satellite data, is a strong predictor of endangered species densities at both scales of analysis. Land use integrates information on habitat loss to agriculture and land use intensity, an index of agricultural pollution. The amount of protected area in a region is unrelated to endangered species numbers except to the extent that areas with the most endangered species are, at best, nearly devoid of protected area. Newly legislated protections for endangered species are unlikely to bring much improvement to this conservation dilemma. Canada's endangered species legislation promotes cooperative conservation activities in areas where species endangerment is most pronounced but does little to protect remaining habitat.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".