10.51847/cE_W_Kp
Bibliographic record
Abstract
Introduction and Objectives: Pests may transmit deadly parasites or pathogens to humans or cause illness, pain and suffering through bites and stings, infected wounds or allergic reactions. Chemical pesticides are considered as the main pest control tool. Insect resistance to the synthetic insecticide occurred following extensive use of this chemical to control pests. Insecticide resistance is a basic threat to urban pest management in the world, to design more applicable Insecticide Resistance Management (IRM) strategies in urban ecosystems. It is necessary to determine the pesticides used for urban pest control and identify various mechanisms underlying insecticide resistance by the pests. Materials and Methods: The review search was performed on the medical and health-related literature. Our search strategy provided us with a total of 374 studies, of which 108 of them were excluded and a total of 266 papers were analyzed and presented in this review article. Results: The common insecticides used to control four urban insect pests: house fly, German cockroach, mosquitoes and scorpions will be described. We will also discuss different mechanisms cussed resistance developed by these pests. Conclusions: The extensive application of insecticides and increasing the pesticide resistance species cause high economic costs and threaten human health. Environment improvement programs to make an undesirable condition for growing arthropods had a reduction effect on the pest population. As well as alternative technologies such as genetic and biological approaches have the potential to control the pest abundance in urban ecosystems. The application of any technology to control the pest to improve human health should not have side-effects on the environment.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.785 | 0.848 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".