Data from: The genetic architecture of UV floral patterning in sunflower
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AbstractBackground and Aims: The patterning of floral ultraviolet (UV) pigmentation varies both intra- and interspecifically in sunflowers and many other plant species, impacts pollinator attraction, and can be critical to reproductive success and crop yields. However, the genetic basis for variation in UV patterning is largely unknown. This study examines the genetic architecture for proportional and absolute size of the UV bullseye in Helianthus argophyllus, a close relative of the domesticated sunflower. Methods: A camera modified to capture UV light (320–380 nm) was used to phenotype floral UV patterning in an F2 mapping population, then quantitative trait loci (QTL) were identified using genotyping-by-sequencing and linkage mapping. The ability of these QTL to predict the UV patterning of natural population individuals was also assessed. Key Results: Proportional UV pigmentation is additively controlled by six moderate effect QTL that are predictive of this phenotype in natural populations. In contrast, UV bullseye size is controlled by a single large effect QTL that also controls flowerhead size and co-localizes with a major flowering time QTL in Helianthus. Conclusions: The co-localization of the UV bullseye size QTL, flowerhead size QTL and a previously known flowering time QTL may indicate a single highly pleiotropic locus or several closely linked loci, which could inhibit UV bullseye size from responding to selection without change in correlated characters. The genetic architecture of proportional UV pigmentation is relatively simple and different from that of UV bullseye size, and so should be able to respond to natural or artificial selection independently.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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 it