Pesticides survey and identification of common insecticides used for foodstuff storage in Makurdi, Benue State, Nigeria
Bibliographic record
Abstract
Pesticides in foodstuffs have become a daily deal with potential health challenges. Identification of these pesticides would be helpful in precautionary ways of dealing with their consequences on foodstuff and human health. A survey of pesticides was done in five major markets located in different axes of Makurdi town. The survey was achieved with the instrument of questionnaire and interview, anchored and recorded during the interview with pesticide sellers at their respective stores at the markets in Makurdi. At least three pesticide dealers were interviewed from each market on the types of pesticides available (inventory) comprehensively, those used for foodstuff storage, effective types of insecticide for foodstuff storage, the most patronized, and their mode of application. Identification and classification of the pesticides were based on active chemical names, common names, or trade names in Nigeria; the nature of active chemicals; applications on the field; and in-store foodstuff. The average percentage of daily patronage was calculated, and knowledge of the expiration date was uncertain. Interestingly, three active chemicals were considered the most popular and sought-after for aiding food storage: aluminum phosphide, dichlorvos, and permethrin, all under multiple brand names. These chemicals accounted for 37.50%, 33.33%, and 20.83% of the market, respectively, with the remaining insecticides accounting for just 8.33%. The study also revealed that many illegal and outdated pesticides are still in use in Makurdi, often in absurd quantities without a shelf life, endangering the health of everyone who consumes the food products obtained from their usage.
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 | 0.000 |
| 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) | 0.002 | 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".