Deployment of Noval Technologies for The Management of White Grubs in Lower Hills of NW Himalyan Region
Bibliographic record
Abstract
White grubs, a group of destructive insect pests of polyphagous nature, cause severe damage to crop plants in hill ecosystem. The grubs with subterranean habitat feed extensively on the roots and the adults defoliate the plants. A two pronged strategies involving an efficient, light weight, eco-friendly, low cost, light based insect trap for capturing the adults and a novel entomo-pathogen, Bacillus cereus strain WGPSB-2 for the management of grubs were developed. Large scale deployment of the above technologies were done on community basis in 5 locations including 4 villages and one experimental farm of Krishi Vigyan Kendra, Uttarkashi district of Uttarakhand. Three years experimentations revealed drastic reduction in beetle population to the tune of 75.8% in low, altitude villages. A significant reduction of the grub population was recorded from 74.11% to 85.17% in three years across the different villages. As a result of reduction in grub population, per cent increase in yield of different crops was recorded from 39.0% to 59.2% in different villages and experimental farm of low hills. The technology is thus, capable of managing white grubs at different altitudes of hills in general and North Western Himalayas in particular.
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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".