Use of diversity arrays technology markers for integration into a cotton reference map and anchoring to a recombinant inbred line map
Bibliographic record
Abstract
A diversity array technology (DArT) marker platform was developed for the cotton genome, to evaluate the use of DArT markers compared with AFLP markers in mapping and transferability across the mapping populations. We used a reference genetic map of tetraploid Gossypium L. that already contained ~5000 loci, which coalesced into 26 chromosomes, to anchor newly developed DArT and AFLP markers with the aim of further improving utility and map resolution. Our results indicated that the percentage of polymorphic DArT markers that could be genetically mapped (78.15%) was much higher than that of AFLP markers (22.28%). Sequence analysis of DArT markers indicated that a majority matched known expressed sequence tag (EST) sequences from tetraploid and diploid Gossypium species. A total of 794 Arabidopsis genes were homologous with various DArT marker sequences. Chromosomes 5(A), 7(A), 19(D), 23(D), and 24(D) had more Arabidopsis syntenic DArT markers than the other chromosomes. Anchoring DArT markers from the reference map to a recombinant inbred line (RIL) map indicated that DArT markers will speed the building of maps in de novo RIL populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".