Effect of temperature, population density, and sex on feeding damage caused by adult pea leaf weevil (Coleoptera: Curculionidae)
Bibliographic record
Abstract
Abstract Sitona lineatus (Linnaeus) (Coleoptera: Curculionidae) is an important pest of Pisum sativum Linnaeus (Fabaceae) and Vicia faba Linnaeus (Fabaceae). Managing S. lineatus is difficult. There is currently no forecast that can be used to predict S. lineatus population densities between growing seasons (when management decisions are made). Sitona lineatus populations are monitored in spring by assessing adult-induced feeding damage. We assume the quantity of feeding damage is indicative of population density. However, the effects of abiotic factors, including temperature, on feeding rate are not known. We assessed the effects of temperature, population density, and sex on S. lineatus feeding rate by counting feeding notches on host plants exposed to adult weevils for 24 hours at four densities and under five temperature regimes. Individual females consumed more than male weevils. As expected, feeding damage increased with weevil density. The influence of temperature increased as weevil densities increased. Feeding rate did not differ between constant and fluctuating temperature regimes of equal mean daily temperature. Thus, a model based on mean daily temperature may predict weevil density if temperature and damage levels are known. Historical survey data for S. lineatus could be used to develop and validate new models.
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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 | 0.001 |
| 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".