Losing history: how extinctions prune features from the tree of life
Bibliographic record
Abstract
Biodiversity provides many valuable services to humanity; however, rapid expansion of the human population has placed increasing pressure on natural systems, and it has been suggested that we may be entering a sixth mass extinction. There is an urgent need, therefore, to prioritize conservation efforts if we are to maintain the provisioning of such service in the future. Phylogenetic diversity (PD), the summed branch lengths that connect species on the tree-of-life, might provide a valuable metric for conservation prioritization because it has been argued to capture feature diversity. Frequently, PD is estimated in millions of years, and therefore implicitly assumes an evolutionary model in which features diverge gradually over time. Here, I explore the expected loss of feature diversity when this assumption is violated. If evolution tends to slow down over time, as might be the case following adaptive radiations, losses of feature diversity might be relatively small. However, if evolution occurs in rapid bursts, following a punctuated model, impacts of extinctions might be much greater. PD captures many important properties, but if we use it as a proxy for feature diversity, we first need to ensure that we have the correct evolutionary model.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".