Prioritizing nationally endemic species for conservation
Bibliographic record
Abstract
Abstract Over 90% of recent human‐caused extinctions are wild species known from only one nation. These nationally endemic species represent one of the greatest global conservation responsibilities for any country. To meet this responsibility, we must first identify nationally endemic species. We developed the first comprehensive inventory of the 308 plant, animal, and fungi species and infraspecies only found in Canada, of which approximately 90% are of global conservation concern. Our analysis also identified 27 spatial concentrations of endemic species, many of which are associated with glacial refugia, islands, coasts, and unique habitats. Nationally endemic species have not been the primary focus of endangered species conservation in Canada and other countries. Our analysis provides a case study on how national inventories of endemic species can be developed and applied to support species assessments and place‐based conservation. Prioritizing endemic species for conservation can build on sentiments of sense of place and national responsibility to foster public interest. We propose a species conservation framework that highlights the critical role of national endemism in preventing global extinctions. Greater conservation focus on endemic species will support national and international biodiversity conservation targets, including the post‐2020 Global Biodiversity Framework.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".