Influence of Herbicide Carrier on the Tolerance of White Bean to Preplant Incorporated and Preemergence Herbicides
Bibliographic record
Abstract
Nine field trials (five with PPI and four with PRE herbicides) were conducted at Exeter and Ridgetown, Ontario during 2013 to 2015 to determine if the tolerance of white bean to preplant incorporated (PPI) and preemergence (PRE) herbicides is influenced by the herbicide carrier (water vs. UAN at 200 L•ha-1). There was no significant interaction between the carrier and herbicide for visible injury, plant stand, plant height, shoot dry weight, seed moisture content and yield. There was also no significant difference between the herbicide carriers for all parameters measured except for the shoot dry weight which was 6.5% greater when UAN was used as the carrier with PPI herbicides. Dimethenamid-p, pendimethalin, imazethapyr and halosulfuron applied PPI or PRE caused no visible injury except for imazethapyr PPI which caused 2% visible injury and dimethenamid-p PRE which caused 7% - 14% injury in white bean. There was no effect of the PPI and PRE herbicides evaluated on white bean stand, shoot dry weight, height, maturity and yield. Based on these results, using water or UAN could be used as the carrier for PPI and PRE herbicides in white bean.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".