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EFFECT OF MEAT APPEARANCE ON SOUTH KOREAN CONSUMERS' CHOICE OF PORK CHOPS DETERMINED BY IMAGE METHODOLOGY

2007· article· en· W2070924767 on OpenAlexaff
Soo‐Hyun Cho, Beom-Young Park, T.M. Ngapo, Jin‐Hyoung Kim, Eric Dransfield, Inho Hwang, Jong-Moon Lee

Bibliographic record

VenueJournal of Sensory Studies · 2007
Typearticle
Languageen
FieldNursing
TopicNutrition, Health and Food Behavior
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsMarbled meatChoseFood scienceAdvertisingBusinessBiologyAnimal science

Abstract

fetched live from OpenAlex

ABSTRACT The effect of appearance characteristics of fresh pork on Korean consumers' preferences was assessed using images of pork chops. Consumers (1015) in six provinces selected their preferred chop from 16 images modified systematically to give two levels each of fat cover, color, marbling and drip. Meat color was the most important characteristic; similar numbers of consumers chose the dark and light red meat colored pork. Almost two‐thirds of the consumers chose consistently the option without drip. A strong characteristic was that almost half of the Korean consumers chose the marbled pork, although fat cover was not an important selection criterion. Similar preferences were observed on a regional basis, except for color, which varied according to region. More female, particularly the married ones, than male consumers preferred the light red, no drip and marbled options.

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)0.003
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.086
GPT teacher head0.417
Teacher spread0.330 · 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

Citations27
Published2007
Admission routes1
Has abstractyes

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