Construction of an evenly-distributed genetic map using contig-tag-SNPs for quantitative trait loci (QTL) analysis of fiber-related traits in kenaf (<i>Hibiscus cannabinus</i> L.)
Bibliographic record
Abstract
Kenaf is one of the most important natural fiber crops worldwide, which aims at harvesting bast fiber. Mining QTL loci of fiber yield and quality traits will facilitate fiber improvement and molecular marker-assisted breeding in kenaf. In this study, Fuhong 952 and Zanyin No. 1 were used as parents to construct two mapping populations, F2 and F2:3, and an evenly distributed genetic linkage map was constructed by re-sequencing. The map contains 2512 contig-tag-SNP markers, and 18 linkage groups with a total length of 1287.63 cM and an average distance of 0.51 cM. Totally, 32 and 28 QTLs were detected in the F2 and F2:3 populations, respectively. Through Blast searching against the reference genome using the sequences of flanking molecular markers linked to QTLs, 374 candidate genes related to cell wall formation and photoperiod regulating flowering were found in these loci, including cellulose synthase-like genes, MYB genes, and Agamous-like genes. These findings could lay a foundation for the improvement of fiber-related traits and gene cloning in kenaf.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".