Weed communities associated with arable Saskatchewan farm management systems
Bibliographic record
Abstract
The objective of this study was to determine the impact of a range of management systems on weed communities in cropped fields on farms in Saskatchewan. Farms (n = 28) with management systems defined by a combination of different cropping histories and chemical input levels were selected. Fields were surveyed on each farm in 1995, 1996, and 1997 after any post-emergent weed control to ensure that their weed communities reflected the impact of all agronomic management practices typical of the management system. Canonical correspondence analysis was used to determine whether weed communities were significantly correlated with management system after removal of variance due to ecoregion and year. The largest difference in weed communities attributable to farming system was between the systems with annual cropping histories and those that included perennials in the cropping history. Thus, the life history of the weeds reflected the life history of the crops. Herbicide use was correlated with the next largest difference between systems. A significant association between weed communities and different management systems indicates that weed species are being selected for by these systems. If such selection pressure continues, these species may become a threat to the system's sustainability. Key words: Weed communities, chemical input level, cropping history, canonical correspondence analysis, correspondence analysis, farming system
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".