Speculation versus data-driven conclusions: A response to Gereau et al.’s "Phylogenetic patterns of extinction risk: the need for critical application of appropriate datasets"
Bibliographic record
Abstract
Gereau et al. (2013) criticized our recent analysis on the phylogenetic patterns of extinction risk in the Eastern Arc biodiversity hotspot (Yessoufou et al. 2012). However, Gereau and colleagues based their critique on preconceptions and speculation rather than data. Here we identify several shortfalls in their lines of argument, and suggest that, given current rates of extinction, it is far more dangerous to wait for complete Red List assessments than to explore patterns of threat using available data. Nonetheless, we agree that all analyses should be based upon the best available data, and we encourage the rapid releases of new data on threat status for the flora of the Eastern Arc.
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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 | 0.302 | 0.553 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.051 |
| Scholarly communication | 0.016 | 0.034 |
| Open science | 0.014 | 0.014 |
| Research integrity | 0.039 | 0.106 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".