Invasive alien plant species aqueous extracts in a war against the granary weevil (Sitophilus granarius [L.]) – are they long-term effective or can they only win the Battle of Cannae?
Bibliographic record
Abstract
This study investigates the effectiveness of plant-based extracts in repelling the granary weevil ( Sitophilus granarius ), a common storage pest causing significant economic losses. The selected plant species (Canadian goldenrod, giant goldenrod, indigo bush, staghorn sumac, and tree of heaven), were examined for their repellent effects on weevils in grain samples. Surprisingly, the short-term repellent effects observed after 24 h diminished after 3 days, suggesting potential challenges for practical applications, especially in long-term grain storage. Temperature emerged as a significant environmental factor, influencing weevil behavior. Lower temperatures created an illusion of increased effectiveness, while higher temperatures accelerated weevil reproduction. Commercial product NeemAzal, despite its reputation for effectiveness, proved the least potent among all treatments, raising questions about its applicability in such cases. Progeny emergence tests indicated no significant differences between treatments, emphasizing the ineffectiveness of the extracts in sustaining long-term repellent effects. The study concludes that while plant-based extracts may offer short-term victories in pest control, their long-term efficacy remains uncertain. The complex interplay of factors, including temperature and humidity, highlights the challenges in developing sustainable and practical solutions for grain storage pest control, necessitating careful consideration by real-world users. • Our study on invasive alien plant-based granary weevil repellents revealed short-term effectiveness. • Progeny emergence tests indicated no significant differences between treatments. • Lower temperatures created an illusion of increased effectiveness, while higher temperatures accelerated weevil reproduction.
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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) | 0.002 | 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".