Characterization and assessment of compost for suppression of selected turfgrass diseases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of composts for turfgrass disease management allows for a reduction of pesticide use in traditional chemical control practices. Up to five composts were characterized and evaluated for suppression of turfgrass diseases. The monitoring of temperature and oxygen throughout the composting process was the best method tested in evaluating compost maturity. Controlled environment experiments with selected compost treatments suppressed dollar spot of turf ('Sclerotinia homoeocarpa' F. T. Bennett) by up to 58% and, in field trials, were not significantly different than fungicide controls ('P' = 0.05). Similarly, fall applications of compost reduced snow mould ('Microdochium nivale' Fr. Samuels and Hallet, ' Typhula ishikariensis' Lasch ex. Fr.) severity to levels not significantly different from fungicide controls and increased green-up of turf (recovery from disease and/or winter dormancy) by up to 63% compared to fungicide and 54% compared to fertilizer controls ('P' = 0.05). Microbial characterization of composts revealed high culturable colony counts. Moreover, 29% of bacteria isolated displayed proteolytic activity. Two bacterial identification systems gave variable results, whereas phospholipid fatty acid (PLFA) analysis was a valuable indicator of microbial community dynamics. Many bacterial isolates tested in the plate challenge experiment displayed antagonistic activity towards selected turfgrass pathogens. Antagonistic activity of composts relies on a number of factors, and although their relative importance varies, microbial activity levels, population dynamics, nutrient aspects, as well as other associated chemical and physical factors all have a part in turfgrass disease suppression.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it