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Record W2945962933 · doi:10.33619/2414-2948/42/26

Laboratory Studies to Identify Listeria in Food Product

2019· article· en· W2945962933 on OpenAlexaboutno aff
O.L. Bereznyak, Е. О. Рысцова

Bibliographic record

VenueBulletin of Science and Practice · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicListeria monocytogenes in Food Safety
Canadian institutionsnot available
Fundersnot available
KeywordsListeriaBusinessListeria monocytogenesFood safetyFood industryPopulationProduction (economics)Food processingFood productsRaw meatBiotechnologyProduct (mathematics)Environmental healthFood scienceBiologyMedicineEconomics

Abstract

fetched live from OpenAlex

One of the main tasks of the domestic meat industry at the present stage of development is to ensure safety for the consumer of produced meat products. It is known that in meat raw materials and products made from it, especially in violation of the technological regimes and sanitary and hygienic conditions of production, it is possible to identify microorganisms dangerous for humans — Listeria. In this regard, in the zone of European economic cooperation, as well as other developed countries (USA, Canada, Japan), the requirements for the control of pathogenic listeria in meat and meat products, the consumption of which can cause human disease, are strictly regulated. The study of food for the presence of the causative agent of listeriosis is mandatory. The problem of food listeriosis is also of significant socioeconomic importance due to the damage caused by the removal of contaminated products, the restriction of exports and imports, and the cessation of production. Laboratory studies are the basis for the prevention of foodborne diseases at all stages of the production of the food industry, which lead to health and safety of the population and the spread of microbiological infections. Taking into account the above, it was necessary to consider the existing developments in the technology of modern nutrient media to identify Listeria.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.672
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 head0.077
GPT teacher head0.398
Teacher spread0.321 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueBulletin of Science and PracticeSame topicListeria monocytogenes in Food SafetyFrench-language works237,207