Glyphosate-resistant Canada fleabane (Conyza canadensis (L.) Cronq.) in Ontario: Distribution and control in soybean (Glycine max (L.) Merr.): Essex, Leamington, Wheatley, and Windsor, Essex County, Ontario [Canada] 2011 and 2012
Bibliographic record
Abstract
This dataset is comprised of two surveys examining the control of glyphosate resistant Canada Fleabane in soybean with 1) preplant herbicide tankmixes and 2) postemergence herbicides. The objective of the first survey was to identify preplant herbicide tankmixes that provide effective control of glyphosate-resistant Canada fleabane. Twelve field trials were conducted over a two year period (2011, 2012) at four farm locations in Essex County, Ontario to evaluate preplant herbicide tankmixes in glyphosate-resistant soybean for the control of Canada fleabane populations previously identified to be resistant to glyphosate. The first trial evaluated herbicides applied preplant with limited residual activity (Enhanced Burndown) while the second trial evaluated herbicides with residual activity for season long weed control (Burndown plus Residual). The objective of the second survey was to evaluate glyphosate and postemergence soybean herbicide efficacy on glyphosate-resistant Canada fleabane. Twelve field trials were conducted over a two year period (2011, 2012) at four farm locations in Essex County, Ontario. The first set of trials evaluated the level of glyphosate resistance in these populations (Biologically Effective Rate of Glyphosate) while the second set evaluated herbicides applied following soybean emergence (Postemergence Tankmixes).
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) | 0.000 | 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".