Efficacy of Home-made and Commercial Trapping Baits for the Management of Fruit Flies in Mandarin Orchards
Bibliographic record
Abstract
The current study was done to appraise the efficacy of different homemade and commercial baits in fruit fly monitoring and examine the lure that attracts fruit flies in citrus orchards at Syangja, Nepal from Feb to June 2022.The two commercial pheromones used in the experiment were Cue Lure 40 mL and Methyl Eugenol 40 mL and the other five home-based baits were Apple Cider Vinegar, Yeast fermented sugar, Mint lure, Local Brewery Liquor and Banana Lure.Lynfield traps with lures were placed in the orchard.The lures were replaced every 15 days and the traps in 50 days intervals.In this experiment, different species of fruit flies were caught; Z. tau, Z. cucurbitae, B. dorsalis, B. dorsalis complex, B.minax, and few counts of Z. scutellaris and B. zonata.The commercial baits used in this experiment in both trappings were able to attract the highest number of fruit flies; all of which were male.Cue lure showed the best result for Zeugodacus species with the highest trapping (68%) of Zeugodacus males while Methyl eugenol trapped a high percentage for Bactrocera species with Bactrocera dorsalis males (63%).Among the homemade baits, ACV trapping was high (16.1%)for male species of Zeugodacus tau, and PH (yeast lure) for Bactrocera minax male species (59%).Moreover, Banana lure was found effective for Bactrocera dorsalis male and ACV for Bactrocera zonata male and female species.In comparing the males and females of Zeugodacus species, cue lure had the better result for trapping males and methyl eugenol for females of Zeugodacus tau sp.Females of both Zeugodacus and Bactrocera species were less trapped in the lures in comparison.
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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".