Examining the plant-back interval for glyphosate/glufosinate-resistant corn after the application of ACCase inhibitors
Bibliographic record
Abstract
Field experiments in 2013 and 2014 examined corn (Zea mays L.) tolerance to acetyl-coenzyme A carboxylase (ACCase) inhibiting herbicides in a scenario where they would have been used to terminate a failed corn stand prior to replanting. To simulate this, herbicides were applied 1 wk or 1 d preplant (PP) and several parameters were measured. Corn injury 1, 2, 4, or 8 wk after emergence (WAE) was similar to the untreated control, regardless of herbicide, rate, or PP application timing. Across herbicides and rates, PP timing did not affect plant stand and aboveground biomass 2 WAE, plant height 4 WAE, or yield. Across application timings, plant stand and aboveground biomass were similar to the untreated control, regardless of herbicide treatment or rate; however, some herbicides reduced height and (or) yield. For example, compared with the untreated control, fluazifop-p-butyl (75 and 150 g ha−1) and sethoxydim (300 g ha−1) each reduced height by about 3%, while clethodim (30 and 60 g ha−1), fluazifop-p-butyl (150 g ha−1), and quizalofop-p-ethyl (72 g ha−1) each reduced yield by about 2%. Therefore, in situations where a grower may need to terminate a failed corn stand, the selection of ACCase-inhibiting herbicides could be based on efficacy rather than plant-back restrictions.
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 | 0.001 | 0.002 |
| 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".