Biologically effective dose of flumioxazin and pyroxasulfone for control of multiple herbicide–resistant waterhemp (<i>Amaranthus tuberculatus</i>) in soybean
Bibliographic record
Abstract
Abstract Two studies were conducted to ascertain the biologically effective dose (BED) of flumioxazin and pyroxasulfone for multiple herbicide–resistant (MHR) waterhemp [Amaranthus tuberculatus (Moq.) Sauer] control in soybean [Glycine max (L.) Merr.] in southwestern Ontario, Canada, during 2016 and 2017. In the flumioxazin study, the predicted flumioxazin doses for 50%, 80%, and 90% MHR A. tuberculatus control were 19, 37, and 59 g ai ha−1 at 2 wk after application (WAA) and 31, 83, and 151 g ai ha−1, respectively, at 12 WAA. The predicted flumioxazin doses to cause 5% and 10% soybean injury were 129 and 404 g ai ha−1, respectively, at 2 wk after emergence (WAE), and the predicted flumioxazin doses to obtain 50%, 80%, and 95% of the weed-free control plot’s yield were determined to be 3, 14, and 65 g ai ha−1, respectively. In the pyroxasulfone study, the predicted pyroxasulfone doses that provided 50%, 80%, and 90% MHR A. tuberculatus visible control were 25, 50, and 88 g ai ha−1 at 2 WAA and 41, 109, and 274 g ai ha−1 at 12 WAA, respectively. The dose of pyroxasulfone predicted for 80% reduction in MHR A. tuberculatus density was 117 g ai ha−1, and the doses of pyroxasulfone predicted for 80% and 90% reduction in A. tuberculatus biomass were 204 and 382 g ai ha−1, respectively. The predicted doses of pyroxasulfone that caused 5% and 10% injury in soybean at 2 WAE were 585 and 698 g ai ha−1, respectively. The predicted doses of pyroxasulfone required to obtain 50%, 80%, and 95% yield relative to the weed-free plots were 6, 24, and 112 g ai ha−1, respectively. Flumioxazin and pyroxasulfone applied preemergence at the appropriate doses provided early-season MHR A. tuberculatus control in soybean.
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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 | 0.000 |
| 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".