Comparison of <i>Macrophomina phaseolina</i> inoculation techniques for screening sunflower and soybean germplasm in a controlled environment
Bibliographic record
Abstract
Macrophomina phaseolina is a polyphagous phytopathogenic fungus that causes serious yield losses in economically important crop plants. The most appropriate control measure for this fungus is the utilization of resistance sources. To achieve this a fast and reliable screening method, in addition to a characterized pathogen population, is a prerequisite to accelerate breeding programmes. The aim of this study was to develop an efficient inoculation method for soybean and sunflower seedlings and to test the effect of inoculum maturity on disease development under controlled environmental conditions. Three inoculation methods, stem-tape, cut-stem and toothpick, and two inoculum types, hyphal and microsclerotial, were tested on sunflower and soybean seedlings. The fastest disease development on sunflower was obtained with the cut-stem and stem-tape inoculation methods using hyphal inoculum, and both methods had high repeatability. The cut-stem method, however, is not applicable for germplasm screening studies due to the predisposition of sunflower seedlings to pathogen attack. Application of the stem-tape inoculation method was useful for a variety of host reaction studies and for rapid germplasm screening. In soybean, the cut-stem inoculation method yielded consistent results in pathogenicity and variety reaction studies, but the stem-tape and toothpick inoculation methods had poor repeatability and long incubation periods, respectively. The cut-stem method in soybean and the stem-tape method in sunflower appear to be the most appropriate for pathogenicity and rapid germplasm screening studies with M. phaseolina under controlled conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".