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Influence of Health and Environmental Information on Hedonic Evaluation of Organic and Conventional Bread

2008· article· en· W2097436456 on OpenAlexafffund
Lisa E. Annett, V. Muralidharan, Peter C. Boxall, Sean B. Cash, Wendy V. Wismer

Bibliographic record

VenueJournal of Food Science · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsAlberta Ministry of Agriculture and ForestryAgriculture Food and Rural DevelopmentUniversity of Alberta
FundersAgriculture and Agri-Food Canada
KeywordsFood sciencePreferenceConsumption (sociology)Probit modelOrganic productionOrganic productMathematicsProduction (economics)Ordered logitAgricultural scienceOrganic farmingPsychologyEconometricsEconomicsStatisticsChemistryEnvironmental scienceGeographyAgriculture

Abstract

fetched live from OpenAlex

Grain from paired samples of the hard red spring wheat cultivar "Park" grown on both conventionally and organically managed land was milled and baked into 60% whole wheat bread. Consumers (n= 384) rated their liking of the bread samples on a 9-point hedonic scale before (blind) and after (labeled) receiving information about organic production. Consumers liked organic bread more (P < 0.05) than conventional bread under blind and labeled conditions. Environmental information about organic production did not impact consumer preference changes for organic bread, but health information coupled with sensory evaluation increased liking of organic bread. Ordinary least squares (OLS) and binary response (probit) regression models identified that postsecondary education, income level, frequency of bread consumption, and proenvironmental attitudes played a significant role in preference changes for organic bread. The techniques used in this study demonstrate that a combination of sensory and econometric techniques strengthens the evaluation of consumer food choice.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.102

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.221
Teacher spread0.202 · 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 designBench or experimental
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

Citations94
Published2008
Admission routes2
Has abstractyes

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