Efficacy of <i>Sclerotinia minor</i> for dandelion control: effect of dandelion accession, age and grass competition
Bibliographic record
Abstract
Summary Control of Taraxacum officinale (common dandelion) and other broad‐leaved weeds in temperate turfgrass has been readily achieved with phenoxy herbicides. The herbicide option has been revoked through municipal and provincial legislation in many regions of Canada, necessitating alternative approaches. We examined the effects of dandelion accessions, age and grass competition on the performance of Sclerotinia minor (IMI 344141) as a biological control for dandelion in turfgrass. Disease symptoms were identical on all 14 different accessions of dandelion and the above‐ and below‐ground biomass were reduced by 94% and 96%, respectively, with no difference among accessions. Foliar damage and dandelion mortality caused by S. minor was affected by plant age and the presence of grass competition. Dandelions of all ages were more severely affected by S. minor in the presence of grass competition. Grass competition had greater impact on foliar biomass, whereas the fungus had a greater impact on root biomass of newly established dandelions. In addition to competition for resources, we were hypothesised that the grass sward provides a microenvironment favouring the success of S. minor as a biological control agent of dandelion. Thus, proper management of the turfgrass environment may be complementary to the efficacy of S. minor as a biocontrol for dandelion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".