Maple Bark Biochar Affects <i>Rhizoctonia solani</i> Metabolism and Increases Damping-Off Severity
Bibliographic record
Abstract
Many studies have investigated the effect of biochar on plant yield, nutrient uptake, and soil microbial populations; however, little work has been done on its effect on soilborne plant diseases. To determine the effect of maple bark biochar on Rhizoctonia damping-off, 11 plant species were grown in a soilless potting substrate amended with different concentrations of biochar and inoculated or not with Rhizoctonia solani anastomosis group 4. Additionally, the effect of biochar amendment on R. solani growth and metabolism in vitro was evaluated. Increasing concentrations of maple bark biochar increased Rhizoctonia damping-off of all 11 plant species. Using multivariate analyses, we observed positive correlations between biochar amendments, disease severity and incidence, abundance of culturable bacterial communities, and physicochemical parameters. Additionally, biochar amendment significantly increased R. solani growth and hyphal extension in vitro, and altered its primary metabolism, notably the mannitol and tricarboxylic acid cycles and the glycolysis pathway. One or several organic compounds present in the biochar, as identified by gas chromatography-mass spectrometry analysis, may be metabolized by R. solani. Taken together, these results indicate that future studies on biochar should focus on the effect of its use as an amendment on soilborne plant pathogens before applying it to soils.
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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 | 0.000 | 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".