Indirect effects of <i>FRIGIDA</i>: floral trait (co)variances are altered by seasonally variable abiotic factors associated with flowering time
Bibliographic record
Abstract
Reproductive timing is a critical life-history event that could influence the (co)variation of traits developing later in ontogeny by regulating exposure to seasonally variable factors. In a field experiment with Arabidopsis thaliana, we explore whether allelic variation at a flowering-time gene of major effect (FRIGIDA) affects (co)variation of floral traits by regulating exposure to photoperiod, temperature, and moisture levels. We detect a positive latitudinal cline in floral organ size among plants with putatively functional FRI alleles. Statistically controlling for bolting day removes the cline, suggesting that seasonal abiotic variation affects floral morphology. Both photoperiod and precipitation at bolting correlate positively with the length of petals, stamens, and pistils. Additionally, floral (co)variances differ significantly across FRI backgrounds, such that the sign of some floral-trait correlations reverses. Subsequent experimental manipulations of photoperiod and water availability demonstrate direct effects of these abiotic factors on floral traits. In sum, these results highlight how the timing of life-history events can affect the expression of traits developing later in ontogeny, and provide some of the first empirical evidence for the effects of major genes on evolutionary potential.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".