Selecting aggressiveness to improve biological control agents efficiency
Bibliographic record
Abstract
Abstract In agroecosystems, omnivorous predators are recognized as potential biological control agents because of the numerous species pest species they prey on. Nonetheless, it could be possible to enhance their efficiency through artificial selection on traits of economical or ecological relevance. Aggressiveness is expected to be related to zoophagy, diet preferences and to a higher attack rate. The study aimed to assess the aggressiveness degree of the damsel bug, Nabis americoferus, and estimate its heritability. We hypothesized that a high aggressiveness degree can be selected, and that males are more aggressive than females. Using artificial selection, we reared two separate populations, each composed of nine genetically isolated lines characterized by their different aggressiveness degree (aggressive, docile and non-selected). After five generations, we had efficiently selected aggressive behavior. The realized heritability was 0.16 (± 0.04 S.E.) and 0.27 (± 0.1 S.E.) for aggressiveness and docility in the first population. It was 0.25 (± 0.03) and 0.23 (± 0.08 S.E.) for the second population. Males were more aggressive than females only for the second population. The potential of these individuals as biological control agents and the ecological consequences of aggressiveness is discussed.
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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 | 0.000 | 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".