Control of Glyphosate-Resistant Giant Ragweed (<i>Ambrosia trifida</i> L.) with Isoxaflutole and Metribuzin Tankmix
Bibliographic record
Abstract
Five field trials were conducted over a two-year period (2013, 2014) to determine the control of glyphosate-resistant (GR) giant ragweed with isoxaflutole (IFT) and metribuzin (MTZ) applied alone and in combination. Treatments were designed to assess the dose response of an IFT plus MTZ tank-mix as well as each chemical applied alone to classify the response using Flint’s adaptation of Colby’s equation. Two factor factorial experiments were performed in the growth room to ascertain the response of IFT versus glyphosate, IFT versus MTZ, and IFT plus MTZ versus glyphosate on single plants. Field experiments evaluated the control of GR giant ragweed with IFT plus MTZ in tank-mix in a 1:4 ratio. The rate of IFT plus MTZ for 80% control of GR giant ragweed at 4 and 8 weeks after application (WAA) was 518 (104 g a.i. ha-1 IFT + 414 g a.i. ha-1 MTZ) and 631 g a.i. ha-1 (126 g a.i. ha-1 IFT + 505 g a.i. ha-1 MTZ), respectively. A rate of 668 and 467 g a.i. ha-1 was required to reduce GR giant ragweed density and biomass by 80%, respectively. Field experiments evaluating the control of GR giant ragweed with tank-mixes of IFT plus MTZ, where glyphosate was a constant tank-mix partner, were mostly synergistic. However, the low tank-mix rate (52.5 + 210 g a.i. ha-1) had an additive response for GR giant ragweed biomass reduction. When tested in the greenhouse and growth room, glyphosate susceptible (GS) giant ragweed showed some antagonism with glyphosate and isoxaflutole tank-mixes at rates less than commercial field rates. GR giant ragweed showed an additive response across all treatments in the growth room. Greenhouse experiments evaluating IFT versus MTZ and IFT plus MTZ versus glyphosate revealed all tank-mix treatments to be synergistic at 2 WAA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".