Field-based evaluation of a novel approach for infecting Canada thistle (<i>Cirsium arvense</i>) with<i>Pseudomonas syringae</i>pv.<i>tagetis</i>
Bibliographic record
Abstract
Canada thistle is resilient to many control tactics, especially in undisturbed sites. Such sites are suitable for slow acting biological control agents, such as the bacteriumPseudomonas syringaepv.tagetis(PST), because complete control is usually not required in the short term. A new method of introducing or intensifying PST infection of Canada thistle was investigated. Sap of naturally infected Canada thistle was extracted and applied in water plus Silwet L-77 organosilicone surfactant with a backpack sprayer to healthy Canada thistle plants in the field. Application variables of time of the season, spray volume, concentration, and frequency were studied. When practical field rates were applied, infected sap concentration and spray volume did not affect the level of disease observed. This suggests that PST applications could be practical at the field scale because a single application caused apical chlorosis. However, multiple applications proved beneficial because four consecutive weekly applications caused greater disease incidence (50%) than one or two applications (28% and 30%, respectively). Disease symptomology was greatest when PST was applied in mid-July rather than mid-June or mid-August. However, the levels of disease expression were not adequate to effectively suppress Canada thistle. Increased toxin production, either by finding ways to support higher PST populations or by selecting strains that produce more toxin per bacterial cell, would improve this 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 | 0.000 |
| 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".