Response of glyphosate-resistant soybean to dicamba spray tank contamination during vegetative and reproductive growth stages
Bibliographic record
Abstract
The anticipated availability of dicamba-resistant crops will increase the potential for crop injury to non-dicamba-resistant soybean due to dicamba spray tank contamination. A total of eight field trials were conducted at various locations in Ontario, Canada during 2012–2014 to determine the response of non-dicamba-resistant soybean to dicamba spray tank contamination at 0, 0.75, 1.5, 3, 6, 15, 30, and 60 g a.e. ha−1 applied postemergence (POST) at the V2-3 (2–3 trifoliate) or R1 (1st flower) stage. At one week after treatment (WAT), dicamba applied at 0.75, 1.5, 3, 6, 15, 30, and 60 g a.e. ha−1 at V2-3 caused 12, 18, 25, 31, 43, 53, and 66% visible injury in soybean, respectively. Injury increased at 2 and 4 WAT and decreased by 8 WAT with 68% visible injury observed at the highest dose. Dicamba applied at R1 caused 23, 28, 36, 40, 48, 61, and 73% visible injury in soybean at 0.75, 1.5, 3, 6, 15, 30, and 60 g a.e. ha−1, respectively. The predicted dose of dicamba to reduce soybean seed yield 1, 5, 10, 20 or 50% was 1.1, 5.8, 11.8, 25.2, and >60 g a.e. ha−1 when applied at V2-3 and <0.75, 1.0, 2.0, 4.3, and 11.5 g a.e. ha−1 when applied at R1, respectively. Results show that dicamba spray tank contamination of as little as 0.75 g a.e. ha−1 can cause significant crop injury in non-dicamba-resistant soybean when applied during the vegetative or reproductive stages.
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".