Extract of Pimenta Racemosa as Attractant for Bactrocera Dorsalis in Mango Orchards
Bibliographic record
Abstract
The status of Bactrocera dorsalis as a destructive pest of fruits including mango continues to be a challenge among farmers at Ghana. Although chemical insecticides have been employed to manage its incidence and damage, there still exist gaps that need be addressed including concerns on toxic residues on fruits and the possibility of resistance evolvement by this pest to insecticides. The alternate management for the fruit fly should therefore be environmentally friendly and with minimal side effects. The objective was to compare the attractiveness of homemade lures of aqueous leaf extract of Pimenta racemosa and a commercial attractant containing methyl eugenol. The research involved the use of leaf extracts of Pimenta racemosa to trap Bactrocera dorsalis was conducted in five mango orchards in two agro-ecological zones in Ghana during the major mango fruiting season of 2017. Three experimental orchards were each sectioned into five blocks of 20 trees each. Four trees in each block formed the sampling trees making 20 sampling trees per orchard received the lures as treatments. The lures were dispensed in homemade traps made of PET containers. A total of 174,388 individual arthropods were captured of which 171,412 were identified as B. dorsalis and 2,976 identified as non-target arthropods. There was a significant difference between the performance of the commercial lure and the leaf extracts (P < 0.05) which was expected. The ability of the Pimenta extracts in the homemade traps to capture some fruit flies is an indication of its potential as a low-cost option to complement the more expensive commercial for small farm holdings.
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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".