Farmers Awareness Concerning Negative Effects of Pesticides on Environment in Jordan
Bibliographic record
Abstract
The main goal of this study was to determine the levels of knowledge of the farmers on the effect of pesticides on environment in Valleys area, Jordan. This is achieved through realizing some secondary objectives such as identification of the farmers' socioeconomic characteristics and its relation to some of the study variables, their attitude towards the negative effects of pesticides on the environment, their knowledge level about pesticides effect and their sources of information about pesticides use, storage and disposal. The study covered valleys areas, and some 98 farmers were included as stratified random sample. The results revealed that the 5% farmers do not rely on agricultural extension but they seek information from other trustworthy sources. The farmers have a positive attitude towards learning about the negative effects of the pesticides on the environment. The study also showed that the most common method of pesticides application is spraying using axi-sprayers or portable sprayers. Strengthen the agricultural extension and increase its effectiveness, promote trust and communication between those who guiding and farmers. Also strengthen the link between agricultural research centers and guiding centers to identify the appropriate type of pesticide to combat, to achieve the best results with least damage. Farmers should be knowledgeable about the importance of continuous medical checking up of their workers and especially those dealing with the agricultural chemicals.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".