Fungal pathogens for biological control of crabgrass «Digitaria spp.» in Canada
Bibliographic record
Abstract
Crabgrass is a major problem in turf in Canada and infestations can be as high as 30% of a residential lawn. Due to the bans and restrictions on the use of chemical herbicides in several provinces, cities and municipalities across Canada, there are currently no effective solutions for controlling crabgrass. Two species of crabgrass, large (Digitaria sanguinalis) and smooth (Digitaria ischaemum), are commonly found in cropland and turf. Several species of phytopathogenic fungi have been studied in China and USA as possible biocontrol agents of Digitaria spp. From among those tested the most promising for the use in Canada are species in the genus Curvularia (C. intermediate, C. lunata, C. eragrostidis). In the present study, twenty-three fungal cultures associated with Digitaria spp. were isolated from leaves with visual symptoms of diseases. They were identified to a genera or species level. Growth and spore production were evaluated for each isolate and slowly growing and poorly sporulating isolates were eliminated from further experiments. Twenty remaining isolates were tested for pathogenicity on large and smooth crabgrass. Isolates belonging to C. eragrostidis species were the most effective. These isolates did not appreciably harm the majority of turf grasses and cereal crops, but caused major damage on forage grass timothy. Due to the absence of difference in the host range and superiority in spore production, isolate Dip0307 (C. eragrostidis) was chosen for further evaluation. Optimal temperature and dew duration conditions and minimal requirements for successful weed control with isolate Dip0307 were determined and compared with those for QZ-2000, the Chinese strain of C. eragrostidis. It was concluded that C. eragrostidis isolate Dip0307 is a strong candidate for development as a bioherbicide against large and smooth crabgrass in Canada.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".