Canada Thistle (<i>Cirsium arvense</i>) and Pasture Forage Responses to Wiping with Various Herbicides
Bibliographic record
Abstract
Weed-wipers may provide effective weed control while minimizing the application of herbicide to nontarget species in rangeland and pasture. To date, few herbicides are recommended for use in weed wiping systems. We assessed Canada thistle and non–Canada thistle herbage responses in two experiments in pastures, the first examining wiped glyphosate, the second comparing glyphosate with three broadleaf herbicides at cost-equivalent concentrations [on a volume to volume (v/v) dilution basis]. In both studies, wiping with a glyphosate solution (33% v/v, equivalent to a one to two dilution ratio of herbicide to water) resulted in lower Canada thistle density and biomass than check plots, with control lasting up to 2 yr. However, significant reductions in grass biomass also occurred and were associated with an increase in the abundance of weedy annual forbs. In contrast, wiping with a concentrated solution of clopyralid (2% v/v), picloram plus 2,4-D (20% v/ v), or 2,4-D plus mecoprop plus dicamba (24% v/v), resulted in similar levels of Canada thistle control but no reduction in grass biomass. Despite direct application of herbicides to tall weeds, clover species in mixed stands were injured. In grass-dominated pastures, wiping with broadleaf herbicides was superior to nonselective glyphosate because the former more effectively balanced Canada thistle control with the retention of grass production.
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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".