The causes of selection on flowering time through male fitness in a hermaphroditic annual plant
Bibliographic record
Abstract
Flowering is a key life-history event whose timing almost certainly affects both male and female fitness, but tests of selection on flowering time through male fitness are few. Such selection may arise from direct effects of flowering time, and indirect effects through covariance between flowering time and the environment experienced during reproduction. To isolate these intrinsically correlated associations, we staggered planting dates of Brassica rapa families with known flowering times, creating populations in which age at flowering (i.e., flowering time genotype) and Julian date of flowering (i.e., flowering time environment) were positively, negatively, or uncorrelated. Genetic paternity analysis revealed that male fitness was not strongly influenced by seasonal environmental changes. Instead, when age and date were uncorrelated, selection through male fitness strongly favored young age at flowering. Strategic sampling offspring for paternity analysis rejected covariance between sire age at flowering and dam quality as the cause of this selection. Results instead suggest a negative association between age at flowering and pollen competitive ability. The manipulation also revealed that, at least in B. rapa, the often-observed correlation between flowering time and flowering duration is environmental, not genetic, in origin.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".