Bottom–up Effects of Crop Seeding Methods on Densities of the Alfalfa Weevil Hypera postica and Its Introduced Parasitoid Bathyplectes anurus
Bibliographic record
Abstract
The alfalfa weevil Hypera postica (Gyllenhal) is the most destructive pest of leguminous crops such as alfalfa, Chinese milk vetch, and hairy vetch throughout the world, including Japan. To control H. postica, the solitary endoparasitoid Bathyplectes anurus (Thomson) was released in Japan as a classical biological control agent. In our study, we investigated the bottom-up effects of hairy vetch seeding methods (i.e., control of seeding timing and density) on the B. anurus density to develop a field production method for B. anurus. The average densities of H. postica and B. anurus were significantly higher with early timing and a higher seeding density compared with early timing and a lower seeding density, late timing and a higher seeding density and late timing, and a lower seeding density. This confirmed that the densities of H. postica and B. anurus could be adjusted by the bottom-up effect of hairy vetch seeding methods. During the tri-trophic interaction among hairy vetch, H. postica, and B. anurus, the interaction between the hairy vetch weight and H. postica densities was high, whereas the interaction between the H. postica and B. anurus densities was very high. These results suggested that early timing and high density seeding indirectly enhanced the density of B. anurus by strong direct effects of the H. postica density on the B. anurus during the tri-trophic interaction. Overall, we concluded that a combination of early timing and high density hairy vetch seeding may facilitate efficient field productions of B. anurus.
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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".