Consumers knowledge regarding pesticides on apples and effective washing to remove the pesticides
Bibliographic record
Abstract

 Objectives: With the increased health awareness, there is a growing demand of fresh produce in food. Not only is there a possibility of the fresh produce to be contaminated with harmful micro-organisms, but also chemicals such as pesticides that have harmful adverse effects. The effective method of washing the fruit can reduce the level of pesticide residue to a significant amount. The objective of the study is to determine if the general public is aware of washing the produce properly and if knowledge, age, gender, education or concerns have any association with the effectiveness of washing. Methods: The study was done using a survey that was designed using Google Forms. An online survey which was self-administered was sent out using snowball sampling. The survey was publicized through both email and social media Facebook. The survey had 19 questions in total 11 of which were general and 8 were knowledge based. The results were analysed by Chi-square test using NCSS Software Package. Results: It was found that there is a statistically significant association between knowledge level and effective method of washing the apples with a p-value of 0.00082. This means H0 is rejected; hence it means there is an associative between knowledge level and effectively washing the apples. No other demographic factors (age, gender, education, concerns, or having children) were found to be associated with the method of washing the produce effectively. Conclusion: It was found through the study that the people who were aware and had good knowledge about the presence of chemicals (pesticides) on apples would wash their fruit (apples) effectively enough that will reduce the pesticide residue on fruits more than people who aren’t aware of the pesticides on fruits. Other factors such as age, gender, preference for the type of food were not found to have any association with washing of the fruit effectively or higher level of knowledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".