Prioritizing nationally endemic species for conservation
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
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.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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