Response of soybean and corn to halauxifen-methyl
Bibliographic record
Abstract
Abstract Preplant (PP) herbicide applications are an important tool within an integrated weed management system, specifically in no-till production. An understanding of crop tolerance regarding PP applications is important for effectively integrating a new herbicide into no-till cropping systems. Twelve field trials (six in corn and six in soybean) were conducted over a 2-yr period (2018 and 2019) near Exeter and Ridgetown, ON. The purpose of these studies was to evaluate the tolerance of soybean and corn to halauxifen-methyl applied PP, PRE, or POST at the registered rate (5 g a.i. ha−1) and twice the registered rate (10 g a.i. ha−1), hereafter referred to as the 1× and 2× rate, respectively. All trials were kept weed-free throughout the growing season to remove the confounding effect of weed interference. Halauxifen-methyl applied 14 d preplant (DPP), 7 DPP, 1 DPP, and 5 d after seeding (DAS) at the 1× and 2× rates caused ≤10% visible soybean injury. In contrast, halauxifen-methyl applied POST (cotyledon–unifoliate stage, VE-VC) caused 67% to 87% visible soybean injury, a 50% to 53% reduction in height, 65% to 81% decrease in population, 56% to 67% lower biomass, and 53% to 63% decline in yield. Halauxifen-methyl applied 10 DPP, 5 DPP, 1 DPP, 5 DAS, and POST (spike–one leaf stage, VE-V1) at the 1× and 2× rate caused ≤3% visible corn injury and caused no effect on corn height or biomass. Halauxifen-methyl applied at VE-V1 at the 2× rate reduced corn yield 10%. Based on these studies, the current application restriction of 7 DPP in soybean and 5 DPP in corn is conservative and could be expanded. Expanding the application window of halauxifen-methyl would increase the utility of this herbicide for producers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".