Investing in evolutionary history: implementing a phylogenetic approach for mammal conservation
Bibliographic record
Abstract
Under the impact of human activity, global extinction rates have risen a thousand times higher than shown in the fossil record. The resources available for conservation are insufficient to prevent the loss of much of the world's threatened biodiversity during this crisis. Conservation planners have been forced to prioritize their protective activities, in the context of great uncertainty. This has become known as 'the agony of choice'. A range of methods have been proposed for prioritizing species for conservation attention; one of the most strongly supported is prioritizing those species that maximize phylogenetic distinctiveness (PD). We evaluate how a composite measure of extinction risk and phylogenetic isolation (EDGE) has been used to prioritize species according to their degree of unique evolutionary history (evolutionary distinctiveness, ED) weighted by conservation urgency (global endangerment, GE). We review PD-based approaches and provide an updated list of EDGE mammals using the 2010 IUCN Red List. We evaluate how robust this method is to changes in phylogenetic uncertainty, knowledge of taxonomy and extinction risk, and examine how mammalian species that rank highly in EDGE score are representative of the collective from which they are drawn.
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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".