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Record W2015106871 · doi:10.5539/mas.v9n2p12

Farmers Awareness Concerning Negative Effects of Pesticides on Environment in Jordan

2014· article· en· W2015106871 on OpenAlexvenueno aff
Mansoor Maitah, Khaled Zidan, Rami Hodrob, Karel Malec

Bibliographic record

VenueModern Applied Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsnot available
Fundersnot available
KeywordsPesticideAgricultureBusinessSample (material)Socioeconomic statusTrustworthinessAgricultural scienceIdentification (biology)Environmental healthEnvironmental scienceGeographyComputer scienceComputer securityMedicineAgronomy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.221
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2014
Admission routes1
Has abstractyes

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