Off-target movement assessment of dicamba in North America
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Six experiments were conducted in 2018 on field sites located in Arkansas, Indiana, Michigan, Nebraska, Ontario, and Wisconsin to evaluate the off-target movement (OTM) of dicamba under field-scale conditions. The highest estimated percentages of dicamba injury in non–dicamba-resistant (DR) soybean were 55%, 44%, 39%, 67%, 15%, and 44% injury for noncovered areas and 55%, 5%, 13%, 42%, 0%, and 41% injury for covered areas during dicamba application in Arkansas, Indiana, Michigan, Nebraska, Ontario, and Wisconsin, respectively. The level of injury generally decreased as the downwind distance increased under covered and noncovered areas at all sites. There was an estimated 10% injury in non-DR soybean at 113, 8, 11, 8, and 8 m; and estimated 1% injury at 293, 28, 71, 15, and 19 m from the edge of treated fields downwind when plants were not covered during dicamba application in Arkansas, Indiana, Michigan, Ontario, and Wisconsin, respectively. Assessment of filter-paper collectors placed from 4 to 137 m downwind from the edge of the sprayed area suggested the dicamba deposition reduced exponentially with distance. The greatest injury to non-DR soybean from dicamba OTM occurred at Nebraska and Arkansas (as far as 250 m). Non-DR soybean injury was greatest adjacent to the dicamba sprayed area, but injury decreased with no injury beyond 20 m downwind or in any other direction from the dicamba sprayed area in Indiana, Michigan, Ontario, and Wisconsin. The presence of soybean injury under covered and noncovered areas during the spray period for primary drift suggests that secondary movement of dicamba was evident at five sites. Additional research is needed to determine the exact forms of secondary movement of dicamba under different environmental conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it