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Record W2804565838 · doi:10.1080/1828051x.2018.1472530

Enrichment tools for undocked heavy pigs: effects on body and gastric lesions and carcase and meat quality parameters

2018· article· en· W2804565838 on OpenAlexaff
Marika Vitali, Eleonora Nannoni, Luca Sardi, Patrizia Bassi, Gianfranco Militerno, L. Faucitano, Alessio Bonaldo, Giovanna Martelli

Bibliographic record

VenueItalian Journal of Animal Science · 2018
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsLoinRandomized block designBody weightMedicineAnimal scienceVeterinary medicineInternal medicineBiology

Abstract

fetched live from OpenAlex

Two independent trials were carried out to assess the effect of two enrichment tools on the prevalence of skin and tail lesions recorded prior of slaughtering, lesions of the pars oesophagea (OGL) of the stomach, and on carcase and meat quality traits in Italian heavy pigs (body weight range: 25–160 kg). Eighty undocked barrows (Landrace × Large White) were used in two trials (20 pigs/experimental group, 5 pigs/pen). In Trial 1, the control group received a hanging metal chain (C1), while the other group received wood logs (WL) placed inside a metal rack. In Trial 2, the control group was provided with hanging chain (C2), while the pen of the other group was enriched with a vegetal edible block (EB) placed inside the metal rack. In both trials, no differences were observed in the prevalence and severity of skin, tail and gastric lesions (p > .05). In Trial 1, WL pigs presented lower backfat (p = .01), higher lean meat percentage (p = .03) and higher drip loss in the loin muscle (p = .02) than C1 pigs. Tail score and gastric lesions showed a moderate correlation (r = 0.42; p = .01) in Trial 1. Treatments had no effect on carcase or meat quality traits in Trial 2 (p > .05). In conclusion, the two enrichments provided did not affect body and gastric lesions, carcase and meat quality of Italian heavy pigs, if compared to the metal chains.

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)0.001
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 score0.863
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.106
GPT teacher head0.393
Teacher spread0.287 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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