Compatibility of commercial fungicide formulations with plant-associated <i>Methylobacterium</i>
Bibliographic record
Abstract
Symbiotic Methylobacterium comprise a significant part of the plant microbiome and are known to benefit host plant growth, development, tolerance to abiotic stress, and enhanced disease resistance. The wide application of commercial broad-spectrum fungicide formulations in contemporary agriculture practices has necessitated the investigation of compatibility between popular pesticide products and bacterial endophytes, especially as the Methylobacterium are increasingly considered for agronomic use, including biocontrol of phytopathogens. This study provides an evaluation of compatibility between a extensive inventory of 40 Methylobacterium strains in response to commercial pesticide formulations, each containing different agtive ingredients: DYNASTY® and QUADRIS® (azoxystrobin), MAXIM®480 (fludioxonil), and APRON XL® LS (metalaxyl-M). Using a diffusion disk assay, no sensitivity of tested Methylobacterium strains could be detected against any fungicide product, at doses within and above the recommended therapeutic window (1–100 µg). Potency of formulations across the same range were confirmed using the sensitive phytopathogen Fusarium graminearum. As Methylobacterium spp. continue to emerge as suitable candidates for various roles in biotechnology, including agriculture, a better understanding on the compatibility between this important genus and commercial fungicide products has become a relevant consideration for integrated pest management practices.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".