Unlocking a Mystery: Characterizing the First Appearance of <i>Clavibacter nebraskensis</i> in Mexican Cornfields
Bibliographic record
Abstract
The Goss’s wilt and leaf blight is a disease of maize (Zea mays) caused by Clavibacter nebraskensis, which was widespread in the last several years throughout the Midwest in the United States, south in Texas, and north to Canada. The bacterium is included within the high-risk list of quarantine pathogens by many plant protection organizations and countries including Mexico. Severe blight symptoms on maize plants were found in different provinces from Coahuila and Tlaxcala, Mexico, in 2012 and 2021, respectively. Twenty bacterial isolates with morphology similar to C. nebraskensis were obtained from the diseased maize leaves. The isolates were confirmed by phenotypic tests and 16S rRNA and gyrB sequencing. Two strains were tested for pathogenicity tests on seven hybrid sweet corn cultivars available in Mexico, and the most sensitive cultivar was tested for all the strains to fulfill Koch’s postulates. The phylogenetic reconstruction based on two single loci reveals a remarkable clustering of Mexican strains to American strains reported approximately 50 years ago. The presence of this pathogen represents a risk and a significant challenge for plant protection strategies in Mexico and maize diversity. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".