Use and application of range mapping in assessing extinction risk in Canada
Bibliographic record
Abstract
ABSTRACT When a species potentially at risk of extinction is considered for legally protected status, changes in geographic range over time are often evaluated and utilized in listing decisions. Range changes are also used to guide conservation management decisions. Although changes in geographic range may provide information relevant to conservation decisions, many factors affect estimates of geographic range. We investigated how geographic range change is estimated and used in the listing process. We evaluated all terrestrial vertebrates in Canada whose assessment as threatened or endangered was based at least partially on small range or range reduction. The 39 cases of birds, mammals, reptiles, and amphibians (including 2 populations of 1 species) used a variety of historical and recent data, including natural history archives, special surveys, and standardized surveys. Little mention was made in the reports of the methods used for range delineation or the limitations of geographic range maps. Of the 39 cases listed by the reports as being threatened or endangered in Canada at least in part due to geographic range, 32 (82%) had ≤10% of their current global range in Canada (i.e., most had wide ranges in the United States), resulting in the listing of species with marginal and sometimes nonviable populations within Canada even when the global population was not at risk. We identify several ways that listing decisions could be improved. © 2015 The Wildlife Society.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".