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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.042
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