MétaCan
Menu
Back to cohort
Record W3158741398 · doi:10.21608/avmj.2019.166432

PREVALENCE OF STAPHYLOCCUS AND AEROMONAS IN SOME SALTED DAIRY PRODUCTS

2019· article· en· W3158741398 on OpenAlexfundno aff
Ahmed Saad, Ehab Salama, HANAN EL DAHSHAN, NOHA TALAAT

Bibliographic record

VenueAssiut Veterinary Medical Journal/Maǧallaẗ Asyūṭ al-ṭibiyyaẗ al-baytariyyaẗ · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsnot available
FundersAssiut UniversityUniversity of TorontoCairo University
KeywordsAeromonasBiologyFood scienceBiotechnologyFisheryBacteria

Abstract

fetched live from OpenAlex

120 samples of pickled white soft cheese (domiata cheese) and Mish were collected from local markets in Port Said governorate, Egypt. Samples were analyzed for sodium chloride level, determination of Staph. Spp at 3% and 10% Na CL, isolation of Staphylococcus aureus at 3% and 10% NaCL and Determination of Aeromonas spp < /em>. at 3% and 10% NaCL. Results obtained revealed that the mean values of the sodium chloride percentage were 3.7 ± 0.13 % in pickled domiata cheese samples and 6.1+0.14 % in mish samples. Incidence of Staph. spp in Pickled domiata cheese was 85% at 3% NaCL and 71.6% at10% NaCL. while in mish samples was 93% at3% NaCL and 83.3% at 10%NaCL. Incidence of Aeromonas spp.at 3% NaCL was nil while at 10 % NaCL was 48.3% in pickled domiata cheese and in Mish 38.3% in. Incidence of A. hydrophila, A. caviea, A.trota and A.schubertii in Pickled domiata cheese were 25.7 %, 40%, 20% and 14.3%, respectively. While in Mish were 25%, 46.4%, 21.4% and 7.1% respectively.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.298
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAssiut Veterinary Medical Journal/Maǧallaẗ Asyūṭ al-ṭibiyyaẗ al-baytariyyaẗSame topicIdentification and Quantification in FoodFrench-language works237,207