Selected cultural and environmental parameters influence disease severity of dandelion caused by the potential bioherbicidal fungi,<i>Phoma herbarum</i>and<i>Phoma exigua</i>
Bibliographic record
Abstract
Selected cultural and environmental variables were investigated for their influence on the efficacy of Phoma herbarum and Phoma exigua to cause disease on dandelion (Taraxacum officinale) under growth room conditions. In both species, mycelial fragments caused significantly greater disease severity on dandelion than spore suspensions. Mycelial age was not an important factor in disease severity caused by P. herbarum, with all cultures causing high disease ratings. However, younger cultures of P. exigua caused the greatest disease severity on dandelion, but significantly less than that caused by P. herbarum. The initial pH of the growth medium (potato dextrose broth) did not affect disease severity caused by either Phoma species. Increasing concentrations of mycelia of P. herbarum were applied to dandelions that were then exposed to various leaf wetness durations. Disease severity increased with increasing leaf wetness duration. For dandelions exposed to no leaf wetness duration, the greater the mycelial concentration, the greater the disease rating. However, for dandelions exposed to all leaf wetness durations, all concentrations of mycelia caused similar disease ratings. As P. herbarum caused high disease ratings on dandelion, it therefore warrants further investigation as a potential bioherbicide for the control of this weed.
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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".