Factors influencing diversification in angiosperms: At the crossroads of intrinsic and extrinsic traits
Bibliographic record
Abstract
Recent studies indicate that both key innovations and available area influence species richness in angiosperms. Available area has been observed to have the greatest effect, however, and appears to alter the "carrying capacity" of a lineage rather than alter diversification rates. Here, we review and weigh the evidence of predictors of angiosperm diversification and further dissect how area can place ecological limits on diversification of angiosperms, specifically addressing the following: (1) theoretical mechanisms by which particular intrinsic and extrinsic traits may affect diversification in angiosperm families; (2) evidence that the amount of available area determines the ecological limits on lineages; and (3) geographical distribution of diversification hotspots in angiosperms, concentrating on the effects of zygomorphy, noncontiguous area, and latitude. While we found that dispersal to numerous noncontiguous areas is most important in spurring diversification, diversification of tropical and zygomorphic families appears to be elevated by the generation of more species per given area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".