Agro-chemical Residue Contamination Profile of Rice-field Mosquito Larval Habitats and Effects on Activities of Insecticide Resistance Marker-Enzymes in Culex quinquefasciatus (Diptera: Culicidae), in an Urban Area of North-central Nigeria
Bibliographic record
Abstract
The ever increasing farming activities and use of synthetic insecticide to control weeds and insects has created multifaceted ecological problem in Minna. These include advent of species of mosquitoes that are resistant to insecticides as well as high insecticide residue in the environment. During this study (June – September, 2017), occurrence and distribution of agro-chemicals in rice farms and level of resistance enzyme in Culex quinquefasciatus were studied. Soil were collected from rice farms where agro-chemicals are used and not used (Control) and subjected to Gas Chromatography /Mass Spectometry (GCMS) analysis. Standard WHO methods were adapted to determine the specific activities of insecticides detoxifying enzymes; esterase, lactate dehydrogenase, alanine transaminase, aspartate transaminase and alkaline phosphatase. The finding of the study established that the bulk of insecticide residue in the soil extracts from rice farm are carbamates, organochlorines, organophosphate and pyrethroids. GCMS analysis revealed organophosphate (monocrotophos) and carbamates (bendiocarb) were higher in abundance. The study also established significant difference in enzyme levels in the mosquitoes. The study demonstrated that pre-exposure of mosquito larvae to agro-chemicals used in farmlands (rice farmlands) can lead to development of cross-resistance to public health insecticides.
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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".