Co-application of glyphosate plus an insecticide or fungicide in glyphosate-resistant soybean
Bibliographic record
Abstract
Soltani, N., Shropshire, C. and Sikkema, P. H. 2012. Co-application of glyphosate plus an insecticide or fungicide in glyphosate-resistant soybean. Can. J. Plant Sci. 92: 297-302. Six field trials were conducted from 2008 to 2010 in Ontario to evaluate soybean injury and weed control efficacy with glyphosate tankmixed with various insecticides or fungicides. There was minimal visual injury (less than 4%) in glyphosate-resistant soybean and no adverse effect on soybean height and yield when cyhalothrin-lambda (Matador®), dimethoate (Lagon®), imidacloprid/deltamethrin (Concept®), spirotetramat (Movento®), pyraclostrobin (Headline®), azoxystrobin (Quadris®), propiconazole (Tilt®), azoxystrobin/propiconazole (Quilt®), tebuconazole (Folicur®) and trifloxystrobin/propiconazole (Stratego®) were tankmixed with glyphosate. Velvetleaf, pigweed species, common ragweed, common lambsquarters and green foxtail control ranged from 91-97, 94-99, 92-99, 80-94 and 98-100%, respectively. However, there was no adverse effect on velvetleaf, pigweed, common ragweed, common lambsquarters and green foxtail control, density and dry weight when one of the insecticides or fungicides evaluated was tankmixed with glyphosate. Based on these results, glyphosate tankmixed with cyhalothrin-lambda, dimethoate, imidacloprid/deltamethrin, spirotetramat, pyraclostrobin, azoxystrobin, propiconazole, azoxystrobin/propiconazole, tebuconazole or trifloxystrobin/propiconazole causes minimal crop injury and has no adverse effect on weed control in glyphosate-resistant soybean under Ontario environmental conditions.
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) | 0.001 | 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".