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Record W6976429603 · doi:10.60692/bc63f-sp111

Microbiological Evaluation of Different Types of Branded and Non-branded Ready-to-Eat Snacks Sold in Elementary Schools of District Peshawar, Pakistan

2023· article· en· W6976429603 on OpenAlexaboutno aff

Bibliographic record

VenueGreater South Information System · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLibraries and Information Services
Canadian institutionsnot available
Fundersnot available
KeywordsHygieneFood safetyFecal coliformHand washingFood contaminantPublic healthFood hygieneFood microbiologyGovernment (linguistics)

Abstract

fetched live from OpenAlex

The lack of hygiene in the preparation and packing of ready-to-eat foods is fast becoming a serious public health concern, especially for school children. The intervention of different pathogenic microorganisms in these foods poses the risk of foodborne disease outbreaks. The current study was designed to assess bacterial contamination in different types of ready-to-eat (branded and non-branded) snacks purchased from September 2021 to December 2021 in various elementary schools of Peshawar, Pakistan. A total of 20 samples were collected and analyzed using the pour plate method for total plate count (TPC). Moreover, the multiple fermentation tube method was used for total coliforms (TC) and fecal coliforms (FC). Escherichia coli isolates were identified using E. coli O157:H7 latex test reagent kit Pro Lab, Canada. The results revealed contamination by TPC (6.67%), TC (40%), FC (33.33%), and E. coli (20%) in the samples. These values were higher than the permissible limits set by Food and Agriculture Organization (FAO). Hence, it was concluded that there is a practice gap in food safety knowledge among ready-to-eat food vendors. The vendors are usually untrained and lack the knowledge of proper hygiene and food handling procedures. It is suggested here that the government should pay special attention towards improving public awareness regarding food safety and quality of ready-to-eat foods sold in Peshawar, Pakistan.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.057
GPT teacher head0.245
Teacher spread0.188 · 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 designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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