Genetic variation among pathotypes of<i>Verticillium dahliae</i>Kleb. from cotton in western Turkey revealed by AFLP
Bibliographic record
Abstract
Cotton (Gossypium hirsutum L.) is crucial for the textile industry worldwide. Among the diseases attacking cotton, Verticillium wilt caused by Verticillium dahliae Kleb. is the most significant. Isolates of V. dahliae can be classified into defoliating and non-defoliating pathotypes. Thirty-two isolates of the non-defoliating pathotype and one isolate of a virulent, defoliating pathotype were analysed by the amplified fragment length polymorphism (AFLP) method. Three hundred and forty AFLP fragments were obtained with nine primer combinations. The number of total bands per primer pair ranged from 16 to 81, with an average of 37.7. The average polymorphism information content (PIC) value for the AFLP products was 0.50. Using the genotypic data, genetic distance analysis was performed. The maximum variation was found between isolates (Vd11) Nazilli and (Vd16) Soke, at a value of 0.79 and the minimum variation was found between isolates (Vd20) Aydin and (Vd14) Soke, at 0.24. The unweighted paired group method with arithmetic averages cluster analysis (UPGMA) was used to discriminate the V. dahliae isolates into five subgroups. Defoliating pathotypes (Vd33) from Soke province formed a single subgroup. As a result, it was found that there was significant variation among Verticillium isolates. AFLP analysis is an efficient and effective marker technology for determining genetic relationships among Verticillium isolates.
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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) | 0.000 | 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 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".