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Record W2786321125 · doi:10.1139/cjas-2017-0148

Carcass characteristics of grass-fed crossbred bulls and steers slaughtered at two different ages

2018· article· en· W2786321125 on OpenAlexvenueno aff
Paulina Pogorzelska‐Przybyłek, Zenon Nogalski, Monika Sobczuk‐Szul, Cezary Purwin, Martyna Momot

Bibliographic record

VenueCanadian Journal of Animal Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsnot available
FundersEuropean Regional Development Fund
KeywordsCrossbreedAnimal scienceLean meatBiologySilageAromaCarcass weightLongissimus ThoracisBody weightFood scienceTenderness

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate beef production potential and meat quality in young crossbred bulls and steers raised indoors and fed grass-based diets. One half of 96 bull calves were castrated at 2–3 wk of age. The calves were fed standard diets, and starting at 7 mo of age, they received grass silage supplemented with concentrates. At the end of the fattening period (18 and 21 mo of age), the animals were slaughtered, carcass quality was evaluated, and samples of musculus longissimus thoracis were collected to determine the proximate composition and meat quality. The semi-intensive feeding system contributed to higher daily gains (0.835 kg) in Holstein-Friesian (HF) × Hereford (HH) crosses. In comparison with steers, bulls were characterized by a higher dressing percentage (+1.33%), higher weight of five most valuable primal cuts (+6.7 kg), and higher lean meat content of three-rib cuts (+7.54%). Meat from steer carcasses, which had higher fat content was more tender and had desirable aroma, juiciness, and flavor. In semi-intensive production systems, steers performed better than bulls, and HF × HH crosses were more suitable than HF × Limousin (LM) and HF × Charolais (CH) crosses. Dairy-beef crosses should be slaughtered at 21 mo of age to improve carcass quality.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.041
GPT teacher head0.262
Teacher spread0.221 · 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 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

Citations14
Published2018
Admission routes1
Has abstractyes

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