Data from: What drives selection on flowering time? An experimental manipulation of the inherent correlation between genotype and environment
Bibliographic record
Abstract
The optimal timing of the seasonal switch from somatic growth to reproduction can depend on an individual's condition at reproduction, the quality of the environment in which it will reproduce, or both. In annual plants, vegetative size (a function of age at flowering) affects resources available for seed production, while exposure to mutualists, antagonists, and abiotic stresses in the environment (functions of Julian date of flowering), influences success in converting resources into offspring. The inherent tight correlation between age, size, and environment obscures their independent fitness contributions. We isolated the fitness effects of these factors by experimentally manipulating the correlation between age at flowering and date of flowering in Brassica rapa. We staggered the planting dates of families with differing ages at flowering to produce experimental populations in which age at flowering and date of flowering were positively-, negatively-, or uncorrelated. In all populations, plants with an early date of flowering produced more seed than those flowering late, regardless of age or size at flowering onset. The temporal environment was thus the principal driver of selection on flowering time, but its importance relative to that of age and size varied with the presence/absence of herbivores and seed predators.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.020 |
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".