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Record W2004805175 · doi:10.1080/10408390701558126

Factors Affecting the Eating Quality of Pork

2008· review· en· W2004805175 on OpenAlexaff
T.M. Ngapo, C. Gariépy

Bibliographic record

VenueCritical Reviews in Food Science and Nutrition · 2008
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsQuality (philosophy)BreedBusinessFood sciencePerceptionBiotechnologyMarketingEnvironmental healthPsychologyMedicineBiologyAnimal science

Abstract

fetched live from OpenAlex

In the last decade studies with the specific objective of improving the sensory quality of pork have come to the forefront of meat research, likely a result of consumer complaints of blandness levelled against modern lean meat and the frequent reference to the more strongly flavored meat that was available years ago. Regardless of the lack of scientific evidence to substantiate or refute these claims, the consumer perception of deteriorated quality is real and presents a challenge for the pork industry. Hence, this review has been undertaken with the aim of providing insight into potential sources of amelioration of the eating quality of fresh pork. Existing works are collated, encompassing animal effects, such as, species, breed, muscle type, fat, and ultimate pH, as well as environmental influences, including pre-slaughter conditions of and housing and exercise, and post-slaughter parameters, such as, electrical stimulation, chilling, and cooking.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.382
GPT teacher head0.434
Teacher spread0.053 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations95
Published2008
Admission routes1
Has abstractyes

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