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Record W4378908335 · doi:10.22207/jpam.17.2.51

Fecal Coliform Bacteria in Vegetable Salads Prepared in Baghdad Restaurants

2023· article· en· W4378908335 on OpenAlexaff
Adil Turki Al-Musawi, Raafat A. Abu-Almaaly, Haider Shannon Kareem

Bibliographic record

VenueJournal of Pure and Applied Microbiology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsCentre for Advancing Health Outcomes
Fundersnot available
KeywordsFecal coliformFecesColiform bacteriaFood scienceSerotypeBiologyVeterinary medicineBacteriaFood contaminantMicrobiologyMedicineWater qualityEcology

Abstract

fetched live from OpenAlex

This study aimed to evaluate the presence of coliforms in 50 samples (25 ready-to-eat vegetable salads and 25 handlers’ hands) collected randomly from restaurants in Baghdad. The total coliform count in the samples of vegetable salads from Al-Sadria and Hay al-Amel reached 4.78 and 4.32 log cfu/g, respectively, whereas those in the swab samples of handlers’ hands from the same areas reached 3.70 and 3.90 log cfu/g, respectively. The percentages of fecal coliform bacteria in the salad samples from Al-Sadria and Hay al-Amel were 35% and 32%, respectively, whereas those in the hand swabs from the same areas were 41% and 36%, respectively. Two isolates of the serotype Escherichia coli O157:H7 were detected in the study samples from the same areas, where the rates of E. coli and fecal coliform bacteria increased. Considering the virulence of this bacterial serotype and its direct impact on consumer safety, we highly recommend implementing quality programs in ready-to-eat vegetable salad production chains, raising the cultural level and health awareness of restaurant owners and workers preparing these salads, and raising public awareness of the potential health risks of consuming contaminated food products.

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 score0.950
Threshold uncertainty score0.167

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 head0.013
GPT teacher head0.213
Teacher spread0.201 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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