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Record W2054738094 · doi:10.4141/a06-072

Effect of feed texture, meal frequency and pre-slaughter fasting on behaviour, stomach content and carcass microbial quality in pigs

2007· article· en· W2054738094 on OpenAlexafffundvenue
Linda Saucier, Dave Bernier, Renée Bergeron, A. Giguère, S. Méthot, L. Faucitano

Bibliographic record

VenueCanadian Journal of Animal Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsAgriculture and Agri-Food CanadaUniversité Laval
FundersAgriculture and Agri-Food Canada
KeywordsMealAnimal scienceStomachBiologyFood science

Abstract

fetched live from OpenAlex

In this study, behaviour in lairage, weight and composition of stomach contents and carcass microbial contamination were studied in 96 barrows assigned to the following treatments: feed texture (FT; mash vs. pellets), meal frequency (MF; 2 vs. five meals per day) and fasting time (WT; 4, 14 and 24 h) according to a 2 × 2 × 3 factorial design. Pigs fed two meals had heavier stomach weights at slaughter than those fed five times per day (P = 0.01). An interaction was found between WT and FT (P = 0.002) for stomach weight. With respect to the contamination of the mouth, total aerobic mesophilic counts were higher than 10 4 cfu cm -2 but not significantly different between treatments. Coprophagy behaviour in lairage was not correlated with mouth contamination at slaughter. The treatment resulting in the lowest Escherichia coli counts on the thoracic area was feeding the pigs pellets five times per day followed by a 24-h fast. In contrast, the highest E. coli counts were observed in pigs fed mash five times per day followed by a 4-h fast. Comparison a posteriori of these two extreme scenarios yielded a P value of 0.03. Key words: Animal behaviour, carcass hygiene, fasting, feeding, pigs, stomach

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)0.002
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 score0.771
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.034
GPT teacher head0.278
Teacher spread0.245 · 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

Citations19
Published2007
Admission routes3
Has abstractyes

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