Genetic diversity in conventional and synthetic wheats with drought and salinity tolerance based on AFLP
Bibliographic record
Abstract
Genetic diversity among 14 drought tolerance (drought accessions) and 27 salinity tolerance (salinity accesions) related conventional and synthetic wheat (Triticum sp.) accessions containing different sources of the D genome was assessed using amplified fragment length polymorphism (AFLP). The wheat accessions were analyzed with 20 EcoRI/MseI primer combinations. Among 918 fragments scored, 368 were polymorphic across all 41 wheat accessions, 348 were polymorphic among the drought accessions and 310 were polymorphic among the salinity accessions. Similarity coefficients among all accessions based on Jaccard’s coefficient ranged from 0.18 to 0.92 with an average of 0.53 ± 0.01; among drought accessions, from 0.16 to 0.79 with an average of 0.43 ± 0.02; and among salinity accessions, from 0.16 to 0.92 with an average of 0.57 ± 0.01. Polymorphic information content (PIC) among all accessions ranged from 0.05 to 0.50 with an average PIC of 0.30 ± 0.01; among drought accessions, from 0.13 to 0.50 with an average PIC of 0.37 ± 0.01; and among salinity accessions, from 0.07 to 0.50 with an average PIC of 0.29 ± 0.01. Cluster and principal component analysis showed distinct groups of accessions both within drought and salinity entries. These accessions possess a substantial amount of genetic diversity and would be very valuable materials for breeding wheat with drought and salinity tolerance. Key words: AFLP, DNA fingerprinting, genetic diversity, synthetic wheat
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".