Occurrence and Distribution of Physiological Races of <i>Exserohilum turcicum</i> in Maize-Growing Regions of Mexico
Bibliographic record
Abstract
Turcicum leaf blight (TLB) is a common foliar disease of maize in Mexico that is caused by the fungal pathogen Exserohilum turcicum. The most effective management strategy against TLB is monogenic race-specific resistance. Among the 140 E. turcicum isolates from symptomatic leaves collected from maize fields in Mexico, 100 were obtained from tropical (Veracruz) and temperate areas (Estado de México) between 2010 and 2019, and 40 isolates were obtained from tropical (Sinaloa, Tamaulipas, Veracruz, and Chiapas), subtropical (Nayarit, Jalisco, and Guanajuato), and temperate areas (Estado de Mexico, Hidalgo, and Puebla) collected in 2019. All the isolates caused TLB symptoms on the positive control (ht4), showing that they were all pathogenic. Six physiological races of E. turcicum (2, 3, 23, 3N, 23N, and 123N) were identified based on resistant or susceptible responses displayed by five maize differential genotypes (A619Ht1, A619Ht2, A619Ht3, B68HtN, and A619ht4). The most common was race 23, accounting for 68% of the isolates, followed by races 23N, 123N, 3, 2, and 3N at 15, 8, 6, 2, and 1%, respectively. Race 123N was able to infect the greatest number of maize differential genotypes used in the study. Race 123N was detected in Sinaloa and Estado de México. Race 3 was detected in Nayarit and Jalisco. Race 2 was detected in Jalisco, Estado de México, and Veracruz, and race 3N was detected in Tamaulipas. Race 23 was equally dominant in the tropical, subtropical, and temperate regions, while race 123N was more common in the tropical environment, and race 23N was more common in the tropical and temperate environments. There was no evidence for shifts in the races between 2010 and 2019.
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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".