Effect of timely application of alternated treatments of Bacillus thuringiensis and neem on agronomical particulars of cabbage
Bibliographic record
Abstract
Diamondback moth (DBM) Plutella xylostella is an economical pest of cabbage. Chemical pesticides constitute so far the major tool for pest management. However, the use of botanical pesticides and microbial is also considered. The objective of this study was to compare the effect of alternating treatments of Bacillus thuringiensis and Neem on agronomic particulars of cabbage as compared to solo and chemical applications. Results showed that the alternation of B. thuringiensis and Neem, performed as well as solo. Agronomic parameters were strongly related to the level of infestation of P. xylostella and other pests. The number of leaves was higher in the control and Dimethoate treatments depicting higher response to severe damages, whereas diameters of cabbage heads were higher in the Biobit and Neem treatments. There was no significant difference between the Biobit and the alternated treatment in terms of weight of cabbage. The diameter of cabbage treated with Biobit was higher than those treated with an alternated treatment. However, there was no significant difference between the alternated treatment and Neem. On the other hand, there was significant correlation between agronomic parameters and the presence of parasitoids. The correlation was significantly greater between the number of leaves, diameter and weight of cabbage in the presence of Oomyzus sokolowskii. These results indicate that timely application of alternated treatments of B. thuringiensis and Neem can be more economically viable as compared to single treatments and should be adopted in integrated pest management programs for cabbage.
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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 | 0.001 |
| 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".