Incorporating Evolutionary Measures into Conservation Prioritization
Bibliographic record
Abstract
Conservation prioritization is dominated by the threat status of candidate species. However, species differ markedly in the shared genetic information they embody, and this information is not taken into account if species are prioritized by threat status alone. We developed a system of prioritization that incorporates both threat status and genetic information and applied it to 9546 species of birds worldwide. We devised a simple measure of a species' genetic value that takes into account the shape of the entire taxonomic tree of birds. This measure approximates the evolutionary history that each species embodies and sums to the phylogenetic diversity of the entire taxonomic tree. We then combined this genetic value with each species' probability of extinction to create a species-specific measure of expected loss of genetic information. The application of our methods to the world's avifauna showed that ranking species by expected loss of genetic information may help preserve bird evolutionary history by upgrading those threatened species with fewer close relatives. We recommend developing a mechanism to incorporate a species' genetic value into the prioritization 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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".