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Record W2160445283 · doi:10.5539/jfr.v4n6p48

Meat Quality in Katahdin Lamb Terminal Crosses Treated with Zilpaterol Hydrochloride

2015· article· en· W2160445283 on OpenAlexvenueno aff
José A. Partida, Tania A. Casaya, M.S. Rubio, R.D. Méndez

Bibliographic record

VenueJournal of Food Research · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPharmacological Effects and Assays
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal scienceBreedChemistrySireBiology

Abstract

fetched live from OpenAlex

<p>The effect of Zilpaterol hydrochloride (ZH) supplementation (0 <em>vs.</em> 0.15 mg/kg live weight) on the meat quality was evaluated in Katahdin x Charollais (32 KCh) and Katahdin x Dorper (28 KD) crosses. Lambs were fed a totally mixed ration with 14% crude protein (CP) and 2.9 Mcal EM/kg DM. Data were analyzed using a completely randomized 2 x 2 factorial design: 2 genotypes (KCh and KD) and 2 ZH levels (0 and 0.15 mg/kg live weight). No interaction was found between ZH and the genotypes. The breed of the sire (BS) did not affect most of the meat traits, only KCh crosses had higher color values (L* = 34.0±0.6 <em>vs.</em> 35.6±0.6; a* = 13.4±0.3 <em>vs.</em> 14.9±0.4; b* = 5.6±0.3 <em>vs.</em> 6.6±0.4; h* = 20.8±0.9 <em>vs.</em> 23.0±0.09; C* = 14.7±0.4 <em>vs.</em> 16.4±0.5), more fat (11.2±0.5 <em>vs.</em> 11.9±0.5) and less protein (21.3±0.1 <em>vs. </em>21.8±0.0) than KD. ZH meat had lower values (P < 0.001) than meat from the control animals: L* (31.9±0.6 <em>vs.</em> 37.7±0.6), a* (12.9±0.4 <em>vs.</em> 15.5±0.4), h* (15.1±0.9 <em>vs.</em> 28.6±0.9) and C* (13.5±0.5 <em>vs.</em> 17.7±0.8). ZH increased shear force on meat (5.4±0.2 <em>vs.</em> 3.7±0.2 kgf), and produced less fat (9.7±0.5 <em>vs.</em> 12.5±0.5%) and bone (23.2±0.3 <em>vs.</em> 24.6±0.2%), but more muscle (65.8±0.5 <em>vs.</em> 61.2±0.4%). Zilpaterol hydrochloride use in lamb production caused leaner yield and more protein retention, at the expense of reducing meat sensory qualities.</p>

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)0.000
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 score0.435
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.222
GPT teacher head0.415
Teacher spread0.193 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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