MétaCan
Menu
Back to cohort
Record W2371277893

Investigation on Conditions of Microbial Contamination in Self-prepared Cold Dishes among Dietary Units in Urban Areas of Nanyang City in 2009

2010· article· en· W2371277893 on OpenAlexaboutno aff
Shuanghui Liu

Bibliographic record

VenueYufang yixue luntan · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsnot available
Fundersnot available
KeywordsCooked meatQuarter (Canadian coin)ContaminationFood scienceFood productsToxicologyBiologyGeographyEcology
DOInot available

Abstract

fetched live from OpenAlex

[Objective]To understand the conditions of microbial contamination in self-prepared cold dishes among dietary units in urban areas of Nanyang city,to provide scientific basis for making measures for food security supervision.[Methods]In 2009,self-prepared cold dishes were collected among dietary units in urban areas of Nanyang city to examine the conditions of microbial contamination.[Results]Examination was made on 821 of 6 kinds of cold dish,the microorganism qualified rate was 62.12%.The examination qualified rate,the barbecue,77.98%,the fried,81.19%,the cooked meat,69.60%,the bean product,65.81%,the cold food in sauce meat dish,43.62%,the cold food in sauce vegetarian dish,54.70%(P0.01).In the first quarter,75.69%,the second quarter,59.29%,the third quarter,49.77%,the fourth quarter,66.33%(P0.01);With the cold dish specially processing room,the qualified rate was 70.57%,with none,it was 48.05%(P0.01).Colony total exceeding the allowed rate was 30.57%,the coli colony exceeding the allowed rate was 26.92%,pathogenic bacteria exceeding the allowed rate was 1.09%.[Conclusion]The microbial contamination is severe in self-prepared cold dishes among dietary units in urban areas of Nanyang city,most severe in sauce meat dish and in sauce vegetarian dish,most serious in the second and the third quarter,especially serious in the cold dish without processing room.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.211
Teacher spread0.189 · 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 designObservational
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

Citations1
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueYufang yixue luntanSame topicFood Safety and HygieneFrench-language works237,207