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Record W1844264558 · doi:10.1002/9781118406281.ch12

Flavouring and Coating Technologies for Preservation and Processing of Foods

2014· other· en· W1844264558 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMicroencapsulation and Drying Processes
Canadian institutionsnot available
Fundersnot available
KeywordsFlavourBusinessFood industryFood scienceTasteFood qualityFood preservationFood packagingFood safetyQuality (philosophy)Food processingEnvironmental scienceCommerceChemistry

Abstract

fetched live from OpenAlex

The food industry, where food processing represents one of the most important stages, always seeks new technologies in order to ensure the quality and safety of food products. Flavour is usually the result of the presence, within complex matrices, of volatile and nonvolatile components with a great range of physicochemical properties. The nonvolatile compounds contribute mainly to the taste while the volatile ones influence both taste and aroma. Edible coatings enhance the quality of food products, protecting them from physical, chemical and biological deterioration. Worldwide, in European countries and in countries like the United States of America (USA), Canada and Australia, food regulation has already existed for decades. Aiming at a global regulation, food additives and contaminants (FAO) have their own guidelines trying to make the food regulation in all countries uniform.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.120

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.237
Teacher spread0.213 · 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

Quick stats

Citations5
Published2014
Admission routes1
Has abstractyes

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