Impact of Puccinia punctiformis on Cirsium arvense performance in a simulated crop sequence
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Bibliographic record
Abstract
Cirsium arvense (Canada thistle) is a perennial weed that causes significant economic losses in agriculture. An extensive rhizomatous root system makes C. arvense difficult to manage, particularly in agricultural systems that use tillage as a primary management tool. There is a need for the development of integrated weed management toolsets that include C. arvense biological controls. Puccinia punctiformis (thistle rust) is an autoecious fungal pathogen that systemically infects C. arvense , with the potential to reduce host vigor over time. The goal of this study was to integrate the P. punctiformis biocontrol with a simulated annual cropping sequence in a greenhouse environment and evaluate C. arvense’s above-and belowground biomass production, and its competitive ability. Repeated P. punctiformis inoculations produced systemically infected C. arvense stems in greenhouse pots over time. Cirsium arvense that was inoculated with P. punctiformis had 1.6 grams/pot ( p = 0.0019 ) less aboveground biomass and 5.6 grams/pot ( p< 0.001) less belowground biomass, compared to the non-inoculated (control). Puccinia punctiformis and crop competition interacted additively to lower aboveground (p<0.001) and belowground (p<0.001) C. arvense biomass more than individual use of either the biocontrol or competition alone. The aboveground competition intensity of C. arvense in a mixed crop sequence, relative to non-inoculated C. arvense grown in a monoculture, was moderately impacted by the P. punctiformis biocontrol ( p = 0.0987 ). These results indicate that systemic infection can reduce biomass production and the competitive ability of C. arvense . Overall, P. punctiformis can be integrated into competitive annual cropping sequences with the potential to reduce C. arvense vigor over time.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it