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Record W2669776767 · doi:10.1111/1541-4337.12273

Effects of High Hydrostatic Pressure Processing on Hen Egg Compounds and Egg Products

2017· article· en· W2669776767 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueComprehensive Reviews in Food Science and Food Safety · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsUniversity of ManitobaUniversité Laval
FundersMitacsEgg Farmers of Canada
KeywordsFood industryHydrostatic pressurePasteurizationFood scienceFood productsFood processingBiochemical engineeringPascalizationChemistryBusinessHigh pressureEngineeringPhysics

Abstract

fetched live from OpenAlex

High hydrostatic pressure (HHP), used alone or with other processes, is an emerging technology increasingly used in the food industry to improve microbial safety, and the functionality and bioactive properties of food products. HHP provides a way to reduce energy requirements for food processing and may contribute to improved energy efficiency in the food industry. Hen egg is used by the food industry to formulate many food products. To improve the microbiological safety of egg and egg-derived products, HHP processing is an attractive alternative to heat- pasteurization and a potential technology. However, HHP treatment induces structural modifications of egg components (such as proteins) which could positively or negatively affect the physicochemical and functional properties of egg-derived products. Improving our knowledge regarding the potential of HHP in the egg industry will add value to the final food products and increase profitability for egg producers and the food industry.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.040
GPT teacher head0.330
Teacher spread0.290 · 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