Evaluating fungi from wood and canola for their ability to decompose canola stubble
Bibliographic record
Abstract
The survival and impact on canola of Leptosphaeria maculans, the causal agent of blackleg, might be reduced by agents that decompose canola stubble. Fifty-six fungal isolates from wood or canola were evaluated for their ability to overcome several biological and (or) physical constraints to their effectiveness in decomposing and (or) eliminating L. maculans from canola stubble. Relative to fungi from canola, wood decay fungi were more tolerant of reduced water potential but somewhat less tolerant of lower temperature. Wood decay fungi were no better than those isolated from canola at decomposing sterile canola stubble and less able than Coprinus spp. and Cyathus olla in colonizing and surviving in nonsterile stubble. None of the isolates were effective in eliminating L. maculans from stubble pieces or causing significant decomposition of nonsterile stubble either under laboratory conditions or in the field. These results suggest that considerable effort would be required to find isolates effective in managing blackleg disease through stubble decomposition. Wood decay fungi would seem to have little potential as biological control agents in this pathosystem because of their inability to colonize and decompose nonsterile canola stubble.
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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) | 0.001 | 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) | 0.001 | 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".