Giant ragweed (Ambrosia trifida L.) control in corn
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it