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Record W4385439590 · doi:10.2478/aspr-2023-0005

Effects of pelvic suspension of beef carcasses and wet aging time of cuts on eating quality and sensory scores of 14 muscles

2023· article· en· W4385439590 on OpenAlexfundno aff
Grzegorz Pogorzelski, Ewelina Pogorzelska-Nowicka, Agnieszka Wierzbicka

Bibliographic record

VenueAnimal Science Papers and Reports · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsnot available
FundersInstitute of GeneticsU.S. Department of Agriculture
KeywordsTendernessLongissimus ThoracisLongissimus dorsiAchilles tendonBreedAnimal scienceIntramuscular fatFlavourBiologyAnatomyFood scienceTendon

Abstract

fetched live from OpenAlex

Abstract To evaluate the effect of carcass hanging method and aging period on beef eating quality young cross-breed bulls were slaughtered in a slaughterhouse in south-eastern Poland. After the slaughter operations were completed, one carcass side was hung by the Achilles tendon and the other by the hip bone. Suspension methods depending on the carcass sides were used in rotation. Consumer samples were prepared from 14 muscles collected from each of the 50 sides. The use of different hanging methods showed their varied impact on tenderness, juiciness, flavour overall liking and eating quality of beef cuts. A positive effect of tenderstretch on eating quality was observed for six of the muscles ( longissimus thoracis, spinalis dorsi, longissimus lumborum, vastus lateralis, gluteus profundus and semimembranosus ), no effect was observed for seven, and a negative effect of suspending carcass by hip bone was noted for just one muscle.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.032
GPT teacher head0.281
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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