Giant ragweed (Ambrosia trifida L.) control in corn
Bibliographic record
Abstract
Soltani, N., Shropshire, C. and Sikkema, P. H. 2011. Giant ragweed (Ambrosia trifidaL.) control in corn. Can. J. Plant Sci. 91: 577-581. Twelve field trials (five with PRE and seven with POST herbicides) were conducted over a 4-yr period (2006-2009) on various Ontario farms with heavy giant ragweed infestations (22 plants m-2) to determine the effectiveness of preemergence (PRE) and postemergence (POST) herbicides for the control of giant ragweed in corn. Atrazine, dicamba, dicamba/atrazine, isoxaflutole plus atrazine, mesotrione plus atrazine, saflufenacil, and saflufenacil/dimethenamid applied PRE provided 9-52, 60-80, 64-83, 44-77, 33-80, 36-80, and 43-63% control of giant ragweed, reduced giant ragweed density 55, 45, 59, 64, 68, 73, and 77% and reduced giant ragweed shoot dry weight 60, 89, 90, 87, 83, 81, and 78%, respectively. Atrazine, dicamba, dicamba/diflufenzopyr, dicamba/atrazine, 2,4-D/atrazine, bromoxynil plus atrazine, prosulfuron plus dicamba, primisulfuron/dicamba, mesotrione plus atrazine, topramezone plus atrazine, and bentazon/atrazine applied POST provided 46-94, 70-90, 69-84, 82-94, 56-83, 59-76, 66-84, 71-81, 49-81, 34-78, and 26-84% control of giant ragweed, reduced giant ragweed density by 65, 82, 71, 82, 76, 76, 59, 65, 59, 47, and 71% and reduced giant ragweed shoot dry weight by 97, 99, 97, 99.6, 98, 98, 95, 97, 95, 88, and 96%, respectively. Based on these results, dicamba/atrazine provided the best and most consistent control of giant ragweed in corn of the PRE and POST herbicides evaluated.
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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".