Herbage Yield Losses in Perennial Pasture Due to Canada Thistle (<i>Cirsium arvense</i>)
Bibliographic record
Abstract
Although the impact of Canada thistle (CT) on annual crop production is relatively well established, few investigations report on this weed's impact within perennial pastures. This field study assessed herbage yield losses within eight central Alberta pastures from 1999 to 2001. Each pasture was sampled in 1999 to quantify thistle and herbage biomass within 25 permanent plots. CT was controlled in 2000 and the response of vegetation measured in 2000 and 2001. Before removal, significant negative relationships (P < 0.05) between thistle abundance and herbage were noted at six sites. After thistle removal, herbage at several sites displayed positive responses. Both thistle density and biomass adequately predicted herbage yield loss. Yield losses due to CT can be substantial, peaking at 2 kg/ha for each kilogram of standing thistle biomass and 4.3 kg/ha with each additional thistle stem per square meter. Demonstrated yield losses were variable among sites however, likely due to factors such as heterogeneity in soils, available moisture, and variation in disturbance history or pasture vegetation composition. CT management in perennial pastures of western Canada may enhance pasture production, but further research is required to reliably predict the ability of pastures to respond.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".