Managing glyphosate-resistant common ragweed (<i>Ambrosia artemisiifolia</i>): effect of glyphosate-phenoxy tank mixes on growth, fecundity, and seed viability
Bibliographic record
Abstract
Common ragweed is one of the most important weeds in the soybean-producing areas of the United States and Canada. Recently, glyphosate-resistant (GR) biotypes have been reported in 15 states and one Canadian province. Reducing the proliferation of GR common ragweed biotypes is complicated by the high fecundity and complex seed germination behavior exhibited by this species. An experiment was conducted to evaluate the efficacy of late herbicide applications for reducing seed production, seed weight, and seed viability of a GR common ragweed biotype. Herbicide treatments included: water control, glyphosate, 2,4-D, dicamba, 2,4-D plus glyphosate, and dicamba plus glyphosate. Treatments were applied at the appearance of male flower buds (Biologische Bundesanstalt, Bundessortenamt and Chemical industry scale [BBCH] 51) or at the early female flowering stage (BBCH 61 to 63). At BBCH 51, 2,4-D or dicamba applied alone or in a tank mix with glyphosate reduced seed production by an average of 80%. Conversely, seed production following these same treatments applied at BBCH 61 to 63 was not significantly different from when glyphosate was applied alone. At this stage of development, all herbicide treatments reduced seed viability relative to the control; however, treatments containing 2,4-D or dicamba resulted in significantly lower viability than when glyphosate was applied alone. These results suggest that the application of tank mixes containing 2,4-D or dicamba have the potential to limit seed production of GR common ragweed when applied on or before BBCH 51. The development of new technologies that facilitate the in-crop application of tank mixes containing 2,4-D or dicamba may therefore be an effective option for limiting population establishment, seedbank replenishment, and future spread of glyphosate-resistant alleles.
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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) | 0.000 | 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".