Bibliographic record
Abstract
Are rates of evolution and speciation fastest where diversity is greatest - the tropics? A commonly accepted theory links the latitudinal diversity gradient to a speciation pump model whereby the tropics produce species at a faster rate than extra-tropical regions. In this issue of Molecular Ecology, Botero et al. () test the speciation pump model using subspecies richness patterns for more than 9000 species of birds and mammals as a proxy for incipient speciation opportunity. Rather than using latitudinal centroids, the authors investigate the role of various environmental correlates of latitude as drivers of subspecies richness. Their key finding points to environmental harshness as a positive predictor of subspecies richness. The authors link high subspecies richness in environmental harsh areas to increased opportunities for geographic range fragmentation and/or faster rates of trait evolution as drivers of incipient speciation. Because environmental harshness generally increases with latitude, these results suggest that opportunity for incipient speciation is lowest where species richness is highest. The authors interpret this finding as incompatible with the view of the tropics as a cradle of diversity. Their results are consistent with a growing body of evidence that reproductive isolation and speciation occur fastest at high latitudes.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".