Multiple herbicide–resistant canola can be controlled by alternative herbicides
Bibliographic record
Abstract
Unintentional herbicide resistance gene stacking in canola may alter the sensitivity of volunteers to herbicides of alternative modes of action commonly used for their control. Greenhouse experiments were conducted to investigate the response of three single-herbicide–resistant (HR) cultivars (glyphosate, glufosinate, imidazolinone), one non-HR cultivar, and seven multiple (double or triple)–HR experimental lines to 2,4-D (amine and ester), MCPA ester, and metribuzin applied at the two- to three-leaf stage and of one non-HR and four HR cultivars (glyphosate, glufosinate, imidazolinone, bromoxynil) to 2,4-D amine applied at two growth stages (two- to three-leaf stage and five- to six-leaf stage). All canola cultivars or lines treated at the two- to three-leaf stage responded similarly to increasing doses of each of the three herbicides. At the five- to six-leaf stage, however, the bromoxynil HR cultivar was less sensitive to 2,4-D than the other cultivars. The results of this study suggest that canola with multiple-herbicide–resistance traits does not differ from cultivars that are non-HR or single HR in its sensitivity to herbicides commonly used to control volunteers. All volunteers, whether non-HR, single HR, or multiple HR, should be treated when plants are most sensitive to herbicides (two- to four-leaf stage) to reduce their interference against crops and their perpetuation of gene flow.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".