The effects of perennial ryegrass overseeding on weed suppression and sward composition
Bibliographic record
Abstract
Field studies were implemented to evaluate overseeding establishment of perennial ryegrass ('Lolium perenne' L.) and its ability to suppress weeds in established Kentucky bluegrass ('Poa pratensis ' L.). One irrigated and one non-irrigated trial were implemented at the Guelph Turfgrass Institute (GTI) and four non-irrigated and one irrigated in-use sites were situated on soccer fields in the Town of Oakville and at the University of Guelph campus. Three application rates of 2, 4, and 8 kg/100m 2 and seven application timings of May, July, September, May+July, May+September, July+September, and May+July+September were examined. Weed cover was not suppressed in 2005 but significant reduction due to overseeding treatments at 4 and 8 kg/l00m2 May+July+September existed in both trials at the GTI in August of 2006. No differences in weed cover were observed on the in-use fields in Oakville or at the University of Guelph campus. Perennial ryegrass populations increased in both GTI trials at all overseeding rates by the completion of the experiment in October 2006 and cover, upwards of 70%, was found in plots that had multiple overseedings at the 8 kg/l00m 2 rate. In-use fields did not exhibit an increase in perennial ryegrass when compared to the control plots.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".