Bibliographic record
Abstract
Underpinning the International Union for Conservation of Nature (IUCN) Red List is the assessment of extinction risk as determined by the size and degree of loss of populations. The IUCN system lists a species as Critically Endangered, Endangered, or Vulnerable if its population size declines 80%, 50%, or 30% within a given time frame. However, effective implementation of the system faces substantial challenges and uncertainty because geographic scale data on population size and long-term dynamics are scarce. I develop a model to quantify extinction risk using a measure based on a species' distribution, a much more readily obtained quantity. The model calculates the loss of the area of occupancy that is equivalent to the loss of a given proportion of a population. It is a very simple yet general model that has no free parameters and is independent of scale. The model predicted well the distributions of 302 tree species at a local scale and the distributions of 348 species of North American land birds. This area-based model provides a solution to the long-standing problem for IUCN assessments of lack of data on population sizes, and thus it will contribute to facilitating the quantification of extinction risk worldwide.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".