Effect of Neem Products and Synthetic Insecticides against Sucking Insect Pests of Cauliflower under Field Conditions
Bibliographic record
Abstract
:A field study was carried out during 2015 at Muhammad Bachal farm at Bakrani District Larkana. Four treatments with three replications were applied. The treatments were: T1=Chemical control (Diamond 20SP), T2=Neem oil, T3= Neem kernel, T4= Untreated (Control). Two insect pests were found infesting Cauliflower including white fliesand thrips. Pre-treatment- and post-treatment observations were recorded. The results revealed that against thrips, the first spray of chemical control (Diamond) showed highest reduction percent (50.61%) followed by neem oil (43.33%), neem kernel (40.42%), and lowest for untreated control (10.31%); while in the second spray also, chemical control (Diamond) showed highest effect against thrips (58.51%); followed by neem oil (57.88%), neem kernel (52.43%) and least by untreated plot (14.77%). Against white flies chemical control (Diamond) showed highest effect (82.89%) as observed during 1st spray, followed by neem oil (72.47%), neem kernel (72.68%), and untreated control (5.12%), while after second spray also chemical control (Diamond) showed highest reduction percent (85.53%) followed by neem oil (74.34%), neem kernel (72.26%), and the lowest was resulted by untreated control (4.11%). Chemical control (Diamond) showed its superiority in effect to combat sucking insect pests studied in cauliflower, followed by neem oil, neem kernel, and untreated control remained the least.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".