NATURAL SELECTION ON PLEIOTROPIC QUANTITATIVE TRAIT LOCI AFFECTING A LIFE-HISTORY TRADE-OFF IN<i>AVENA BARBATA</i>
Bibliographic record
Abstract
We applied QTL mapping to fitness variation of Avena barbata under well-watered greenhouse conditions. One hundred eighty recombinant inbred lines were assayed for flowering time, total size, mass allocation, and fitness. Composite Interval Mapping identified two to five loci affecting these traits. These were well supported in more powerful Multiple and Bayesian interval mapping analyses that indicated that additional QTL, as well as epistatic interactions also affect the traits. The posterior distribution of the number of QTL peaked at five to eight additive loci and one to two interactions, but the specific locations of the additional loci could not be determined with certainty. In most cases in which loci for separate traits mapped to similar locations, explicit tests supported pleiotropy over close linkage of separate loci. Alleles that hastened first flowering generally reduced vegetative mass, increased reproductive mass, and were associated with high fitness. Because effects on mass allocation generally cancelled one another, few loci affected total plant size. Only one QTL affected vegetative mass independent of reproductive mass and this locus had little effect on fitness. Thus selection acts to shift the mass allocation toward greater reproductive allocation, because the correlated decrease in vegetative mass poses only a minor fitness cost.
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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 | 0.000 | 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) | 0.000 | 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".