Strategies to control Canada thistle (<i>Cirsium arvense</i>) under organic farming conditions
Bibliographic record
Abstract
Abstract Three strategies for controllingCirsium arvenseincluding (i) repeated stubble tillage with subsequent forage crop cultivation, (ii) repeated mowing of a ryegrass–clover ley and (iii) forage crop cultivation following a ryegrass–clover ley ploughed in May/June were investigated in field experiments over 3 years at the Experimental Farm for Organic Agriculture ‘Wiesengut’ in North-Rhine Westphalia, Germany. The development ofC. arvense(shoot density, shoot size and ground cover) was regularly assessed on fixed standardized subplots. In the medium-term (9 months), repeated stubble tillage (i) decreased shoot density and regrowth capacity ofC. arvensemore effectively than a mowed ryegrass–clover ley (ii and iii). However, after 22 months, strategies (i) and (ii) resulted in a similar strong reduction ofC. arvenseshoot density of 95 and 97%, respectively. At this time, the efficacy of strategy (iii) (89%) was not significantly different to that of strategies (i) and (ii). After 26 months, the effect of all strategies was still apparent; however, the efficacy of strategy (iii) was significantly lower than that of strategy (ii). Generally, the different strategies showed only minor differences, thus delivering options for optimal strategies of thistle control under given specific conditions of sites and cropping systems.
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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".