Isoxaflutole and metribuzin interactions in isoxaflutole-resistant soybean
Bibliographic record
Abstract
Abstract Herbicide-resistant weeds are a growing concern globally; in response, new herbicide resistance traits are being inserted into crops. Isoxaflutole-resistant soybean [Glycine max (L.) Merr.] will provide a new mode of action for use in this crop. Ten experiments were conducted over a 2-yr period (2017, 2018) to determine herbicide interactions between isoxaflutole and metribuzin on soybean injury, weed control efficacy, and soybean yield on a range of soil types. Soybean leaf-bleaching injury caused by isoxaflutole was most severe at sites with higher levels of rainfall after application. Control of weed species with isoxaflutole (52.5, 79, and 105 g ai ha−1) and metribuzin (210, 315, and 420 g ai ha−1) differed by site based on amount of rainfall after application. At sites where there was sufficient rainfall for herbicide activation, isoxaflutole at all rates controlled common lambsquarters (Chenopodium album L.), Amaranthus spp., common ragweed (Ambrosia artemisiifolia L.), and velvetleaf (Abutilon theophrasti Medik.) >90%; metribuzin at all rates controlled Amaranthus spp. and witchgrass (Panicum capillare L.) >80%. Control of every weed species evaluated was reduced when there was limited rainfall after herbicide application. The co-application of isoxaflutole + metribuzin resulted in additive or synergistic interactions for the control of C. album, Amaranthus spp., A. artemisiifolia, A. theophrasti, Setaria spp., barnyardgrass [Echinochloa crus-galli (L.) P. Beauv], and P. capillare. Isoxaflutole and metribuzin can be an effective management strategy for common annual broadleaf and grass weeds in Ontario if timely rainfall events occur after herbicide application.
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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) | 0.001 | 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 | 0.001 |
| 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".