Avoiding conflicts between insect and weed biological control: selection of non‐target species to assess host specificity of cabbage seedpod weevil parasitoids
Bibliographic record
Abstract
Abstract: Classical biological control of insect pests and weeds may lead to potential conflicts, where insect pests are closely related to weed biological control agents. Such a conflict may occur in the classical biological control of the cabbage seedpod weevil, Ceutorhynchus obstrictus (Marsham) in North America, which belongs to the same subfamily, Ceutorhynchinae, as a number of agents introduced or proposed for introduction against non‐indigenous invasive weed species. We propose a step‐by‐step procedure to select non‐target species and thereby to develop a non‐target species test list for screening candidate entomophagous biological control agents of a herbivore pest insect in a way that would simultaneously evaluate non‐target potential on weed biological control agents and other non‐target species. Using these recommendations, we developed a non‐target test list for host specificity evaluations in the area of origin (Europe) and the area of introduction (North America) for cabbage seedpod weevil parasitoids. Scientifically based predictions on expected host–parasitoid interactions and ecological information about the ecological host range in the area of origin can help avoid conflicts, while still allowing the introduction of safe and effective agents against both insect pests and weeds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".