Identification of the most conservative plant species for promising natural enemies of arthropods pests of Vegetable crops
Bibliographic record
Abstract
Conservative plants were identified from three different districts of Kashmir viz: Srinagar, Ganderbal and Budgam during 2016-2018. A total of 20 different plant species suitable for the important natural enemies were identified. Carrot family (caraway, coriander, wild carrot, dill, fennel), butter cup, buckwheat, dandelion, yarrow, Canada thistle, may weed act as attract for the predators. Marigold, dill, coriander and onion were act as repellent plants for insect pests of vegetables. Maize was act as barrier crop for aphids and flying insects and cowpea act as alternate host for egg laying of borer and cut worms that help in avoid damage in main crops. The conservative plants that attract beneficials against Cruciferous and Solanaceous insect Pests were identified. Maximum population of predators and parasitoids were recorded in the month of May in Dill, coriander, onion and wild carrot. While as maximum population of predators and parasitoids were recorded in the month of July-August in Marigold, fennel, dandelion, clover and cowpea. Flowering periods of conservative plants play important role in the activity of natural enemies. The visiting periods of different kinds of natural enemies for conservative plants ranges from February to November, whereas the majority of the natural enemies visit the flowers from the month of April to September during both years.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".