Development of an Ultra-Dense Genetic Map of the Sunflower Genome Based on Single-Feature Polymorphisms
Bibliographic record
Abstract
The development of ultra-dense genetic maps has the potential to facilitate detailed comparative genomic analyses and whole genome sequence assemblies. Here we describe the use of a custom Affymetrix GeneChip containing nearly 2.4 million features (25 bp sequences) targeting 86,023 unigenes from sunflower (Helianthus annuus L.) and related species to test for single-feature polymorphisms (SFPs) in a recombinant inbred line (RIL) mapping population derived from a cross between confectionery and oilseed sunflower lines (RHA280×RHA801). We then employed an existing genetic map derived from this same population to rigorously filter out low quality data and place 67,486 features corresponding to 22,481 unigenes on the sunflower genetic map. The resulting map contains a substantial fraction of all sunflower genes and will thus facilitate a number of downstream applications, including genome assembly and the identification of candidate genes underlying QTL or traits of interest.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".