Non‐dicamba‐resistant soybean response to multiple dicamba applications
Bibliographic record
Abstract
Abstract The rapid adoption of dicamba (3,6‐dichloro‐2‐methoxybenzoic acid)‐resistant (DR) soybean [Glycine max (L.) Merr.] resulted in an increase of post‐emergent dicamba applications during the soybean‐growing season, resulting in off‐target movement and injury to non‐DR soybean. Field trials were established in Manhattan, KS, in 2018 and 2019 and in Ottawa, KS, in 2019 to characterize the response of non‐DR soybean to one, two, or three applications of reduced rates of dicamba at three application timings. Soybean were treated with 0.56, 1.12, and 5.6 g a.e. ha−1 of dicamba, which is equivalent to 1/1,000X, 1/500X, and 1/100X of a 1X field‐use rate (560 g a.e. ha−1), respectively. Soybean plants were treated at V3, R1, R3, V3 followed by (fb) R1, V3 fb R3, R1 fb R3, and V3 fb R1 fb R3 growth stages. Soybean injury from dicamba was less severe following application during the V3 than the R1 or R3 growth stages. In general soybean injury was the greatest 4 wk after application. The greatest soybean yield reduction (68%) followed dicamba applications of 5.6 g a.e. ha−1 at V3 fb R1 fb R3 in Manhattan, KS, 2018, where yield loss was generally greater and may be attributed to droughty conditions. Yield loss was minimal in Manhattan, KS, and Ottawa, KS, in 2019 following a single dicamba application at the V3 stage, regardless of application rate and following dicamba application at 0.56 g a.e. ha−1, regardless of number of applications. The greatest soybean yield losses from dicamba occurred with two or three applications at 1.12 or 5.6 g a.e. ha−1.
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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 | 0.001 |
| 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".