Explaining the apparent paradox of persistent selection for early flowering
Bibliographic record
Abstract
Decades of observation in natural plant populations have revealed pervasive phenotypic selection for early flowering onset. This consistent pattern seems at odds with life-history theory, which predicts stabilizing selection on age and size at reproduction. Why is selection for later flowering rare? Moreover, extensive evidence demonstrates that flowering time can and does evolve. What maintains ongoing directional selection for early flowering? Several non-mutually exclusive processes can help to reconcile the apparent paradox of selection for early flowering. We outline four: selection through other fitness components may counter observed fecundity selection for early flowering; asymmetry in the flowering-time-fitness function may make selection for later flowering hard to detect; flowering time and fitness may be condition-dependent; and selection on flowering duration is largely unaccounted for. In this Viewpoint, we develop these four mechanisms, and highlight areas where further study will improve our understanding of flowering-time evolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".