Bibliographic record
Abstract
The genus Aquilegia consists of 60-70 perennial plant species widely distributed throughout the northern hemisphere. Its flowers have a delicate and ornamental appearance that makes them a favourite of gardeners. In this genus, adaptive radiations for both floral and vegetative traits have occurred. These adaptive radiations, and the key phylogenetic placement of Aquilegia between Arabidopsis and rice, make this genus a 'model system' for plant evolution (Kramer 2009). In this issue, Castellanos et al. (2011) use a marker-based method to infer heritability for floral and vegetative traits in two Aquilegia species. Layered on top of this are estimates of the strength of natural selection. This novel joint estimation of heritability and selection in the wild showed that vegetative traits, compared to floral traits, have the highest evolutionarily potential. Evolutionary potential is the most important quantity to measure in wild populations. It combines inheritance and strength of selection and predicts the potential for populations to adapt to changing environments. The combination of molecular techniques with species in natural environments makes this work a model for molecular ecological investigations.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".