Dark extinction: The problem of unknown historical extinctions
Bibliographic record
Abstract
AbstractThe extinction of species before they are discovered and named (dark extinction, DE) is widely inferred as a significant part of species loss in the ‘pre-taxonomic’ period (approx. 1500–1800 CE) and, to some extent, in the ‘taxonomic period’ (approx. 1800–present) as well. The discovery of oceanic islands and other pristine habitats by European navigators and the consequent introduction of destructive mammals, such as rats and goats, started a process of anthropogenic extinction. Much ecosystem change happened before systematic scientific recording, so has led to DE. Statistical methods are available to robustly estimate DE in the ‘taxonomic period’. For the ‘pre-taxonomic period’, simple extrapolation can be used. The application of these techniques to world birds, for example, suggests that approximately 56 DEs occurred in the ‘taxonomic period’ (1800–present) and approximately 180 in the ‘pre-taxonomic period’ (1500–1800). Targeting collection activities in extinction hotspots, to make sure organisms are represented in collections before their extinction, is one way of reducing the number of extinct species without a physical record (providing that collection efforts do not themselves contribute to species extinction).
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".