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Record W2763849502 · doi:10.1139/cjas-2017-0032

Effects of slaughter weight and growth rate on the <i>longissimus</i> muscle metabolic characteristics, and pork sensory quality in pigs of two sexes

2017· article· en· W2763849502 on OpenAlexaffvenue
L. Faucitano, J. Rivest, Nancy Graveline, Simon Cliché, C. Gariépy

Bibliographic record

VenueCanadian Journal of Animal Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCalpastatinLongissimus muscleAnimal scienceLongissimusSensory systemLactate dehydrogenaseBiologyFood scienceCarcass weightBody weightTendernessEndocrinologyBiochemistryEnzymeCalpain

Abstract

fetched live from OpenAlex

A total of 119 pigs were used to investigate the effect of slaughter weight (107, 115, and 125 kg), growth rate (fast vs. slow), and sex (barrows vs. gilts) on the longissimus muscle biochemical and sensory traits. Increasing slaughter weight to 125 kg resulted in greater postmortem activity of calpastatin (P = 0.01), lactate dehydrogenase (P = 0.01), and citrate synthase (P = 0.02). Pork toughness and juiciness at 6 d were affected by the interaction slaughter weight × growth rate × sex, with pork being tougher (P = 0.04) and juicer (P = 0.03) in slow-growing gilts slaughtered at 125 and 115 kg, respectively. Flavour was scored higher (P = 0.03) in pork from gilts than from barrows. Overall, based on the slight and likely undetectable differences by the average consumer in the meat sensory traits, it can be concluded that slaughter weight can be increased to 125 kg without appreciable effect on the sensory properties.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.261
Teacher spread0.228 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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