Pathogenic variability of <i>Verticillium dahliae</i> isolates from potato fields in Manitoba and screening of bacteria for their biocontrol
Bibliographic record
Abstract
Verticillium dahliae causes wilt disease in many crops, including potato (Solanum tuberosum). Very few strategies were developed to date to control this disease and are either harmful to the environment and human health or inefficient in reducing inoculum levels in the soil. Establishing a biocontrol strategy, as part of an integrated management program, would be an eco-friendly alternative to reduce the incidence of this disease in potato. The present study evaluated the proportion of V. dahliae among Verticillium spp. recovered from samples of potato collected in Manitoba fields, on the basis of morphological characteristics and polymerase chain reaction assays. The present study also assessed the pathogenic variability among the collected isolates. Over 90% of the recovered Verticillium isolates were found to be V. dahliae. Artificial inoculation of 'Russet Burbank' potato plants with these isolates showed a high degree of pathogenic variability among them, according to their differential ability to progress upward in the plant and cause wilt and browning of the vascular system. The most pathogenic isolates were selected for further experiments where they were challenged with bacteria from a collection of bacterial isolates with potential biocontrol activity. Three of the 18 bacteria initially screened provided a strong inhibition (>50% compared with the control) of V. dahliae growth in vitro. These bacteria were initially isolated from rhizosphere soils and represent potential biocontrol agents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".