Influence of Spring Tillage and Glyphosate Treatment on Dandelion (<i>Taraxacum officinale</i>) Control in Glyphosate-Resistant Canola
Bibliographic record
Abstract
Although dandelion has been recognized for some time as a common weed of perennial plant stands, recent weed surveys in western Canada indicate that dandelion has become more common in fields where annual spring crops are grown. Because little has been published on dandelion control in annual crops, a field study was conducted at two locations in southern Manitoba in 1999 and 2000 investigating the effect of spring tillage, glyphosate dosage, and application timing on dandelion control in a spring annual glyphosate-resistant canola crop. The experiments were situated in areas known to have natural infestations of dandelion. Final assessments of dandelion control were performed the spring following treatment, i.e., the next year, to provide a better indication of treatment efficacy on this perennial weed. Spring tillage alone did not significantly reduce dandelion density, as assessed the following spring, but did reduce dandelion shoot dry matter at three of the five site-years by up to 84%. Glyphosate was applied preplant, in-crop, and postharvest at dosages ranging from 450 g ae/ha in-crop to 2,700 g ae/ha postharvest. Glyphosate application after the canola crop had been harvested provided the greatest level of dandelion control, with a single postharvest application of 900 g ae/ha of glyphosate reducing dandelion density and shoot dry matter by 88 and 96%, respectively, the following spring. Applications of glyphosate either preplant or in-crop were not nearly as effective as the postharvest treatments in reducing dandelion density and shoot dry matter the following spring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".