To Evaluate The Bioefficacy Of Several Insecticides Against The Brinjal Shoot AndFruit Borer (Leucinodes Orbonalis Guen.)
Bibliographic record
Abstract
During Kharif 2022, an experiment was carried out in the brinjal field (Cultivar- Makra) at the Central Research Farm of Bidhan Chandra Krishi Viswavidyalaya, Gayeshpur, Nadia, West Bengal, to investigate the field efficiency of several pesticides against the brinjal shoot and fruit borer. Nine treatments (including control) i.e., Diflubenzuron+Deltamethrin 20% WP + 2% SC @ 2.25 ml/3L, Spinosad 45 SC @ 0.1ml/3L, Indoxacarb 14.5 SC @ 0.3 ml/3L, Diflubenzuron 25% WP @ 2.4 ml/3L, Deltamethrin 2.8% EC @ 2.13 ml/3L, Cypermethrin+Quinalphos 3%+20% EC @ 3.6 ml/3L, Deltamethrin+Triazophos 1% WP+35% EC @ 7.5 ml/3L, Emamectin Benzoate 5 SG @ 0.20 ml/3L were imposed with three replications in RBD. All the insecticidal treatments were significantly superior to the untreated control. Spinosad 45 SC was discovered to be the best-performing pesticide treatment against shoot and fruit borer with the lowest percentage of fruit loss (10.04%). Deltamethrin 2.8% EC was the poorest effective treatment when compared to the other treatments because it generated the highest fruit destruction (13.88%). In terms of yield Spinosad 45 SC performed well than all other treatments (227.05 q/ha) and Deltamethrin 2.8% EC was the lowest (176.10 q/ha).
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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) | 0.001 | 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".