Tolerance of new seedings of different grass species to application of NEU1173H and efficacy of NEU1173H for weed control in newly seeded turf - 2011.
Bibliographic record
Abstract
The objective of this research was to assess the tolerance of new seedings of different grass species to application and efficacy of the herbicide NEU1173H. Plots were located at the Guelph Turfgrass Institute, in a tilled area that had been in mixed turf and white clover cover. The treatments were combinations of different grass species, timings, and rates of postemergent herbicide, as well as controls. The effects of timing of application on grass was quite different between species. Fine fescues did better with a later application, while perennial ryegrass did slightly better with an early application. Timing had less effect on Kentucky bluegrass.The early application was likely phytotoxic to the grasses, while the late application allowed the heavy weed pressure to suppress grass growth. Early application led to better grass development in the perennial ryegrass stand. NEU1173H suppressed weeds in a very heavily infested seedbed situation, but there were important differences, particularly between species and timing treatments. Perennial ryegrass was very competitive against weeds, and even the untreated plots were more than 75% grass by the end of the season. Even at the lowest rate of NEU1173H increased grass to more than 95%, and the ryegrass plots showed only slight differences between rates and timings. Kentucky bluegrass was less competitive, though even the untreated plots were 62% grass by the end of the season. The treatments had a bigger effect on Kentucky bluegrass than on the ryegrass, with the highest rate and best timing increasing the grass to over 85%.
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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".