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Record W3109842240 · doi:10.1186/s43014-020-00040-y

Natural bioactive substances for the control of food-borne viruses and contaminants in food

2020· review· en· W3109842240 on OpenAlexaff
Yao Pan, Zeyuan Deng, Fereidoon Shahidi

Bibliographic record

VenueFood Production Processing and Nutrition · 2020
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicEssential Oils and Antimicrobial Activity
Canadian institutionsMemorial University of Newfoundland
FundersChina Scholarship CouncilNortheastern States Research Cooperative
KeywordsContaminationNatural (archaeology)Natural foodContaminated foodFood contaminantEnvironmental scienceFood scienceBiochemical engineeringBiologyMicrobiologyEcologyEngineering

Abstract

fetched live from OpenAlex

Abstract: Food-borne viruses and contaminants, as an important global food safety problem, are caused by chemical, microbiological, zoonotic, and other risk factors that represent a health hazard. Natural bioactive substances, originating from plants, animals, or microorganisms, might offer the possibility of preventing and controlling food-borne diseases. In this contribution, the common bioactive substances such as polyphenols, essential oils, proteins, and polysaccharides which are effective in the prevention and treatment of food-borne viruses and contaminants are discussed. Meanwhile, the preventive effects of natural bioactive substances and the possible mechanisms involved in food protection are discussed and detailed. The application and potential effects of natural bioactive substances in the adjuvant treatment for food-borne diseases is also described.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.042
GPT teacher head0.273
Teacher spread0.231 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations20
Published2020
Admission routes1
Has abstractyes

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