Construction of a High‐Density Genetic Map and Its Application to QTL Identification for Fiber Strength in Upland Cotton
Why this work is in the frame
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Bibliographic record
Abstract
Cotton ( Gossypium sp.) is an important worldwide cash crop that provides a competitive renewable natural fiber supply for the demands of textile industry. The development of new textile technologies and the improvement of living standards increase the demands for both fiber quantity and fiber quality. ‘0–153’ is an upland cotton cultivar with excellent fiber quality derived from Asiatic cotton sources, especially with regards to fiber strength. To identify quantitative trait loci (QTLs) for fiber strength in this line, a recombinant inbred line population consisting of 196 lines was developed from a cross between it and ‘sGK9708’. A genetic linkage map consisting of 2393 loci was constructed using this recombinant inbred line population, with single nucleotide polymorphism (SNP) markers from the IntlCottonSNPConsortium_70k chip. Quantitative trait loci for fiber strength were detected across 11 environments using both single‐environment and combined multiple‐environment models. A total of 63 QTLs controlling fiber strength were detected by the single‐environment model. Sixteen QTLs were identified by the combined multiple‐environment model. These QTLs could make a contribution to the improvement of fiber quality via marker‐assisted selection and provide useful information for QTL fine mapping and functional gene research activities as well.
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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 | 0.000 |
| 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 it