Increasing the Efficacy and Extending the Effective Application Period of a Granular Turf Bioherbicide by Covering with Jute Fabric
Bibliographic record
Abstract
Progress in bioherbicide development has been hindered by the strict moisture and temperature requirements of the living active ingredient. Application of a jute fabric to areas treated with aSclerotinia minorgranular bioherbicide improved broadleaf weed control and broadened the effective application period to include the warm summer season. When turfgrass plots treated with the bioherbicide were covered with burlap fabric for 3 d, broadleaf weed (dandelion, white clover, broadleaf plantain, buckhorn plantain, ground ivy, and prostrate knotweed) control was greatly enhanced. The cover was made of natural jute fibers that retained water but had sufficient transparency to allow 33% light penetration for continued growth of the grass. Virulence of the bioherbicide was maintained under elevated temperatures that would otherwise reduce efficacy. The bioherbicide was ineffective in the summer unless covered, but dandelion density, broadleaf weed ground cover, and dandelion survival were all reduced by the bioherbicide when plots were covered, even if applications were made in July. The efficacy of the bioherbicide was also enhanced under favorable conditions, and covering permitted reduced application rates without loss of efficacy. When applied at a rate of 20 g/m2and covered,S. minorgranules exerted significantly greater biocontrol of dandelion than 40 g/m2without covering. Covering for up to 5 d did not cause any adverse effects on the turfgrass. This approach may overcome one obstacle to the commercialization of theSclerotinia minorbioherbicide, permitting its deployment under challenging environmental 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 | 0.000 | 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".