Canada Thistle (<i>Cirsium arvense</i>) Affects Herbage Production in the Northern Great Plains
Bibliographic record
Abstract
Canada thistle can cause greater than 50% yield loss in small grain crops, but little is known about production losses when the weed invades pasture and wildlands. Change in grass, forb, and woody species production from Canada thistle infestations was evaluated in two separate studies in North Dakota. The first measured change in production following aminopyralid applied at 120 g ha−1to control Canada thistle at two prairie sites. In general, grass, broadleaf, woody, and total plant yields were similar between treated and untreated prairie, regardless of the near-complete control of Canada thistle following aminopyralid application. Grass yield increased by 365 kg ha−1the year after treatment at one location, with no change in forb or woody species production. Plant production was also estimated at 20 ungrazed wildland preserves located within two Major Land Resource Areas (MLRAs). Similar to the prairie sites, minimal differences in production between Canada thistle–infested and noninfested sites were observed. The only exception was an increase in grass production of 425 kg ha−1at one of the MLRAs, with no change in broadleaf or woody species production between the Canada thistle–infested and noninfested sites. In contrast to cropland, pasture and wildland production of other species was not consistently reduced by Canada thistle.
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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) | 0.001 | 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".