Glyphosate-Resistant Cropping Systems in Ontario: Multivariate and Nominal Trait-Based Weed Community Structure
Bibliographic record
Abstract
Glyphosate-resistant (GR) cropping systems are popular and used extensively by producers. However, the long-term impacts of heavy reliance of this technology on weed community structure are not known. Five fully phased field experiments (two no-tillage and three conventional tillage) were established at four locations in southwestern Ontario where the effects of herbicide system (glyphosate or conventional) in corn and soybean and crop rotation (corn–soybean or corn–soybean–winter wheat) on midseason weed communities were examined. Multivariate analysis on data over the last 3 yr of the 6-yr experiment showed that weed communities were distinctly different among the treatments within each experiment. At several locations, midseason weed communities were more similar in corn and soybean treated with glyphosate compared to the same crops treated with conventional herbicides, reflecting the continuous application of the same selection pressure in both crops. Analysis of trait-densities revealed an increase in species with late initiation of seedling recruitment at the expense of weed species with medium time of initiation of seedling recruitment rather than early recruiting species. Increases in perennial species, species with a short interval between recruitment and anthesis, and wind-dispersed species were also observed. Trait-density–based analysis of the weed community was an effective method for reducing the complexity of divergent weed communities that enabled direct quantitative comparison of the herbicide-induced effects on these weed communities.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Bench or experimental | low |
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 | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".