The role of sticky yellow traps in reducing the population of the Dubas Bug, Ommatissus lybicus.
Bibliographic record
Abstract
The Ommatissus lybicus (Dubas Bug), causes compensation every year, is one of the key pests of palm areas of the country. With respect to the common method of inhibiting chemical control of pests and prevent their damage the broad-spectrum insecticide, the broad-spectrum insecticide, and how the use of high risks to the environment and on human health problem. According to severe damage in addition to new and low-risk pesticides and other solutions evaluated. In this study, the effects of sticky yellow traps in reducing the population of Dubas Bug, was assessed in a randomized complete block design 5 treatments included four types of traps colored yellow (English yellow trap, Korean yellow trap, Iranian yellow trap & Canadian yellow trap) and colorless sticky traps (Transparent) as control with four replications. The field experiment was conducted from the spring 2013 to 2015 on palm dates in Fars province. The results showed that the Korean yellow sticky trap with an average of 316.78 ± 8.18 insects per trap had the best efficiency in attracting all the insects of the Dubas Bug. In the second year, the yellow sticky tape trap was used to catch insects. The results showed that the English yellow sticky tape at a height of three meters with an average of 81.26 ± 9.24 insects per trap and the Korean yellow tape trap at a height of three meters from the ground with an average of 72.33 ± 11.23 insects per trap. They have had the best effect in reducing the Dubas Bug. Finally, according to the results of the installing of the sticky yellow trap card and yellow-roll trap, were effective in reducing the Dubas Bug insects.
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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".