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Record W4311255517 · doi:10.1016/j.psj.2022.102423

Research Note: Effects of fiber source on the physicochemical properties of lean poultry meat products

2022· article· en· W4311255517 on OpenAlexafffund
Shai Barbut

Bibliographic record

VenuePoultry Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsFood sciencePoultry meatFiberDietary fiberChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Plant fiber addition (citrus A and B, apple, pea, bamboo, and sugar cane) to lean turkey meat was evaluated and texture, yield, and microstructure were compared to a control. The best 2 were the sugar cane and apple peel fibers which reduced cooking loss, and increased hardness by 20% compared to the control. The bamboo fibers significantly improved hardness but not yield, while the citrus A and apple fibers reduced cooking loss but did not affect hardness. The differences in the effect of fiber type on texture appear to be related to their origin (e.g., sugar cane and bamboo originating from large plants requiring strong fibers, compared citrus and apple fruits), and fiber length determined by the fiber extraction procedure.

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 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)0.003
metaresearch head score (Gemma)0.002
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 score0.017
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.084
GPT teacher head0.293
Teacher spread0.209 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations10
Published2022
Admission routes2
Has abstractyes

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