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Record W2040869557 · doi:10.1080/00071660701691284

Effect of hydrolysed and regular dairy proteins on the texture, colour and microstructure of poultry meat emulsions

2007· article· en· W2040869557 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

VenueBritish Poultry Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Guelph
FundersDairy Farmers of Ontario
KeywordsFood scienceHydrolysateWhey proteinChemistryWhey protein isolateIngredientTexture (cosmology)HydrolysisBiochemistry

Abstract

fetched live from OpenAlex

1. The effects of 2% whole milk proteins, milk protein's two major components (caseinate and whey) and some of their fractions (beta-lactoglobulin and two hydrolysed whey proteins) were evaluated in a frankfurter-type product made from mechanically deboned chicken meat. 2. Adding caseinate, regular whey protein and the two whey protein hydrolysates (WPH-I and II) significantly reduced cooking loss compared to the control; lowest reduction was obtained with the two hydrolysates. 3. Caseinate and regular whey increased hardness, but WPH-I and II substantially reduced hardness, fracturability and springiness. 4. The more hydrolysed whey (8.5% vs 5.2%; WPH-II vs WPH-I, respectively) caused a greater disruption to texture, which could be explained by the light micrographs showing an interference with meat protein binding. 5. The most cost-effective ingredient tested here was the caseinate.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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