Interactions between Two Biological Control Agents and an Herbicide for Canada Thistle (<i>Cirsium arvense</i>) Suppression
Bibliographic record
Abstract
Abstract We investigated the single and combined effects of two biological control agents, the stem-mining weevilHadroplontus lituraand the pathogenPseudomonas syringaepv.tagetis, with a herbicide (reduced or full application of glyphosate: 0.63 kg ae ha−1, or 3.78 kg ae ha−1, respectively) on the growth of Canada thistle,Cirsium arvense. We hypothesized that first, although each control method would have a negative effect on Canada thistle shoot biomass, root biomass, and shoot number, the integration of more than one control method would have greater impact than individual control methods. Second, we hypothesized that the order in which control methods are applied affects the outcome of the management program, with a pathogen application following weevil infestation being more effective than one prior to it. Although control methods impacted Canada thistle growth (P < 0.001, expect for a nonsignificant impact of glyphosate on shoot number), the combined effect of the three control methods behaved, generally, in an additive manner. A marginal interaction between the pathogen and the herbicide (P = 0.052) indicated a slight antagonistic interaction between these control methods. An interaction between the two biological control agents tested (P < 0.001) indicated that application of a pathogen prior to the release of weevil larvae could be more deleterious to Canada thistle than a late application. The observed, mostly additive, relationship between biological control agents and herbicides implies that integrating control methods rather than using a single approach could lead to greater Canada thistle control.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".