Analysis of physiological races and genetic diversity of <i>Setosphaeria turcica</i> (Luttr.) K.J. Leonard & Suggs from different regions of China
Bibliographic record
Abstract
Setosphaeria turcica causes Northern corn leaf blight (NCLB). In this study, 92 isolates of S. turcica were collected from naturally infected corn fields at 57 sites within China to determine physiological race composition and genetic diversity. Based on the reaction of differential hosts, isolates were divided into 14 physiological races (0, 1, 2, 12, 3, 13, 23, N, 1N, 2N, 3N, 13N, 23N, 123N). Races 0 and 1 were dominant, found with frequencies of 34.78% and 28.26%, respectively. This study was the first to identify race 123N in Heilongjiang province, implying possible loss of corn variety resistance to the NCLB pathogen. A total of 64 loci were obtained from eight pairs of primers by sequence-related amplified polymorphism (SRAP), of which 44 were polymorphic, accounting for 68.75% of the loci. Molecular markers showed that 92 isolates could be categorized into four groups with a similarity coefficient of 0.82, indicating abundant genetic diversity. Further analysis of genetic similarity and genetic distance of each geographical population revealed that the populations from Northeast, North, and Northwest China exhibited high similarities to each other, while exhibiting a large genetic distance with those from Southwest China. Analysis of molecular variance indicated that 81.54% of the genetic variation among isolates was derived from individuals within the geographical population (P < 0.001). The cluster analyses suggested that there was no distinct correlation among physiological races, genetic variation and geographic sources. This study provides a basis for understanding trends in S. turcica distribution and control of NCLB in China.
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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".