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Record W2211150303 · doi:10.1071/an14987

Carcass cut-out value and eating quality of longissimus muscle from serially harvested savannah-raised Brahman-influenced cattle and water buffaloes in Venezuela

2015· article· en· W2211150303 on OpenAlexaff
Nelson Huerta-Leidenz, Argenis Rodas‐González, Argelis Vidal, Juan Carlos López-Núñez, O. Colina

Bibliographic record

VenueAnimal Production Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBrahmanAnimal scienceBiologyEnvironmental management systemCrossbreedWeaningVeterinary medicineBreedAgronomyIrrigationMedicine

Abstract

fetched live from OpenAlex

Males (n = 132) of riverine water buffalo (Buffalo) and Brahman-influenced cattle (Brahman) were reared alike and serially harvested at four different ages (7, 17, 19 and 24 months) to compare cutting yield (%), eating quality and consumer acceptability of cube-roll steaks at 19 and 24 months of age (MOA), and to examine post-weaning castration effects. Brahman bulls outperformed Brahman steers and Buffalo male classes in the proportion of chuck-roll, medium-value and total valuable cuts (P < 0.05). At all harvest ages, Buffalo carcasses yielded higher (P < 0.05) percentages of trimmed fat, which resulted in a sustained decline of the proportion of total lean, edible cuts. Buffalo meat had a lower shear-force value and a higher proportion of tender steaks than did Brahman at 7 and 24 MOA (P < 0.05). Whereas trained panellists detected differences in sensorial attributes only at 7 months [when Buffalo steaks were rated as more tender and flavourful (P < 0.05) than Brahman steaks], consumer acceptability ratings for Buffalo meat trended to be higher when harvested at 19 and 24 MOA (P < 0.1). The increasing proportion of boneless lean cuts with age gives Brahman a clear, commercial advantage over Buffalo; however, Buffalo produces meat as juicy and flavourful as that of Brahman and exhibits superior eating quality if harvested at 7 or 24 MOA.

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)0.001
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 score0.713
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.099
GPT teacher head0.319
Teacher spread0.220 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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