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Record W1954042885 · doi:10.4141/cjas10078

Effect of intended purpose (dairy/beef production) on beef quality

2011· article· en· W1954042885 on OpenAlexvenueno aff
G. Lizaso, M.J. Beriain, A. Horcada, J. Chasco, A. Purroy

Bibliographic record

VenueCanadian Journal of Animal Science · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsnot available
FundersInstituto Nacional de Investigación y Tecnología Agraria y Alimentaria
KeywordsTendernessBreedAnimal scienceLongissimus dorsiFood scienceWater holding capacityBeef cattleChemistryBiology

Abstract

fetched live from OpenAlex

Lizaso, G., Beriain, M a . J., Horcada, A., Chasco, J. and Purroy, A. 2011. Effect of intended purpose (dairy/beef production) on beef quality. Can. J. Anim. Sci. 91: 97–102. Fifteen Pirenaica (beef) heifers and fifteen Holstein-Friesian (dairy) heifers were compared to consider the effect of intended purpose of the breed on meat quality. Animals were slaughtered at around 12.4 mo of age. Carcass weight was 247.6 kg for Pirenaica and 229.2 kg for Holstein-Friesian heifers. pH 24hours , moisture, fat, protein, ash, water holding capacity, CIE L*a*b* and C* and H* values, collagen content, and shear force were determined in longissimus dorsi muscle. Sensory analysis of meat was performed to obtain the texture profile, scored by a trained taste panel. Sensorial differences of meat were mainly in relation to higher fat content observed in Holstein-Friesian (2.87%) vs. Pirenaica breed (1.82%) (P<0.01). Total and soluble collagen contents for the two breeds differed (P<0.01 and P<0.05, respectively) but did not result in different meat tenderness scores by taste panelists. Higher water-holding capacity in meat of Holstein-Friesian was observed (P<0.01). CIE L*a*b*, coordinates C* and H* values were not different between breeds. Sustained juiciness and oiliness were higher in Holstein-Friesian than Pirenaica (P<0.05 and P<0.01, respectively). Results suggest that beef characteristics of young heifers of dairy and beef breeds are similar with the exception of characteristics related to juiciness.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 science0.0010.000
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 head0.068
GPT teacher head0.282
Teacher spread0.213 · 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

Citations17
Published2011
Admission routes1
Has abstractyes

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