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Record W2897272430 · doi:10.1139/cjas-2018-0085

Influence of the sex and type of tissue on the basic chemical composition and the content of minerals in the sirloin and offal of fattener pigs

2018· article· en· W2897272430 on OpenAlexvenueno aff
Marek Babicz, Anna Kasprzyk, Kinga Kropiwiec-Domańska

Bibliographic record

VenueCanadian Journal of Animal Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsnot available
Fundersnot available
KeywordsFood scienceChemistryComposition (language)Chemical composition

Abstract

fetched live from OpenAlex

The aim of the study was to determine the basic chemical composition and mineral content in the sirloin and offal of fattener pigs, taking their sex into consideration. In addition, comparative analysis of the content of chemical and mineral components in the sirloin and offal was performed. The experimental material included 24 crossbred (Polish Landrace × Polish Large White) fatteners, from which samples of sirloin (musculus longissimus lumborum), liver, heart, and kidneys were collected for the analysis. A significant effect of the sex on the fat content in the sirloin and liver and on the level of magnesium (Mg), zinc, and iron (Fe) in the sirloin was demonstrated. Significant differences were found in the basic chemical composition and mineral content between the sirloin and the offal. In comparison with the liver, the sirloin contained lower amounts of protein and fat and higher potassium and Mg contents. In turn, the offal was shown to be a rich source of sodium, calcium, and trace elements (Fe, manganese, and copper). The mean cadmium concentration in the sirloin and offal was significantly lower than the threshold values for these elements, i.e., it accounted for 26% and 6.2% (liver)–19.5% (heart) of the allowable content of this element in pork meat and offal.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.060
GPT teacher head0.256
Teacher spread0.196 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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