Glyphosate-resistant giant ragweed (Ambrosia trifida L.) in Ontario: Survey and control in soybean (Glycine max L.) Windsor, Belle River, LaSalle, and Amherstburg, Essex County, Ontario [Canada] 2010 and 2011
Bibliographic record
Abstract
This dataset is comprised of two surveys examining the control of glyphosate resistant giant ragweed (Ambrosia trifida) in soybean (Glycine max) with 1) preplant herbicides and 2) postemergence herbicides. The objective of the first survey was to evaluate the efficacy of various preplant herbicides for the control of glyphosate-resistant giant ragweed under field conditions in Ontario. A total of 11 field experiments were established on Ontario farms with glyphosate-resistant giant ragweed over a two-year period (2010 and 2011). One set of experiments evaluated herbicides with limited residual activity (enhanced burndown), and a second set of experiments evaluated glyphosate tank mixes with residual herbicides for full season control (burndown plus residual). In 2010, there was one enhanced burndown and one burndown plus residual trial at a location near Windsor (L1). In 2011, there were four enhanced burndown and five burndown plus residual trials at locations near Windsor (L2 and L3), Belle River (L4), LaSalle (L5) and Amherstburg (L6). The objective of the postemergence survey was to determine the level of resistance to glyphosate in different giant ragweed populations, and evaluate the efficacy of various postemergence herbicides for the control of glyphosate-resistant giant ragweed in soybean under field conditions in Ontario. A total of ten field experiments were established on Ontario farms with glyphosate-resistant giant ragweed in 2011. One set of experiments evaluated the response of giant ragweed to varying doses of glyphosate (field dose response), and another set evaluated various herbicides registered for postemergence application in soybean. The experiments were conducted at locations near Windsor (L1 and L2), Belle River (L3), LaSalle (L4) and Amherstburg (L5).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".