Subindividual variation and genetic versus environmental effects on seed traits in a European<i>Aquilegia</i>
Bibliographic record
Abstract
Repetitive plant parts such as seeds can be characterized at the within-individual level not only by average values but also by higher moments of their distributions, such as the standard deviation, skewness, and kurtosis. These parameters can potentially be adaptive and shaped by selection like any other individual trait, but tend to be understudied. We present an example of how within-individual variation might be studied. We used a diallel crossing design to investigate the sources of phenotypic variance of among- and within-individual variation in seed and germination traits of the endemic columbine Aquilegia pyrenaica subsp. cazorlensis (Heywood) Galiano & Rivas Martínez. Individual plants and even fruits have their characteristic mean, standard deviation, skewness, and kurtosis and therefore these parameters could potentially respond to selection. However, we only found evidence of genetic variance in mean within-fruit seed mass, mean time to seedling emergence, and number of seeds: these effects were attributable to maternal effects. Variance in higher moments of the within-fruit distributions in this species could instead reflect developmental instability or plastic responses to localized environmental conditions as fruits develop. Large sample sizes are required to make final conclusions in this case, but this study illustrates how the novel approach of considering high moments of intra-individual variation will enhance our view of plant trait evolution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".