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Record W4390048577 · doi:10.3390/gels10010007

Textural Restoration of Broiler Breast Fillets with Spaghetti Meat Myopathy, Using Two Alginate Gels Systems

2023· article· en· W4390048577 on OpenAlex
Chaoyue Wang, Leonardo Susta, Shai Barbut

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

VenueGels · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsFood scienceChicken breastBroilerPenetration (warfare)ChemistryRaw meatCooked meatRaw materialSodium alginateMathematicsSodiumOrganic chemistry

Abstract

fetched live from OpenAlex

The effects of salt-sensitive alginate (“A”) and a two-component salt-tolerant alginate system (“B”) used at a 0.5% or 1.0% level were evaluated in normal breast (NB) chicken fillets and in spaghetti meat (SM) fillets. Minced raw and cooked SM samples showed higher cooking loss (p < 0.05) and lower penetration force compared to NB meat. Both alginate systems significantly raised the penetration force in raw samples and decreased cooking loss (p < 0.05). Adding 1% of “A” or 0.5% “B” to SM, without salt, resulted in a similar penetration force as the cooked NB meat, while 1% “B” with salt resulted in a higher penetration force. Excluding salt from SM samples while adding alginate “A” or “B” improved texture profiles, but not to the same level as using NB without additives. Overall, salt, together with alginate “B”, improved the texture of SM to that of normal meat without myopathy.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.196

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 head0.057
GPT teacher head0.265
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