Consumer purchase intentions for pork with enhanced carnosine–A functional food
Bibliographic record
Abstract
Abstract In this study, Canadian consumers’ preferences for enhanced carnosine (a naturally occurring dipeptide that exhibits antiaging properties) in pork are examined. Carnosine is a relatively unknown nutrient to the public such that we are interested in understanding the relative merits of informing consumers of enhanced carnosine levels through a possible health claim, a nutrient content claim or including it in the nutrition facts table (NFT). As a basis of comparison, we include two other possible labels, a protein nutrient content claim, and a Verified Canadian Pork label (created by industry identifying food safety, animal care, traceability, and farm to table quality assurance attributes of the production system). Data were collected through an online survey that included a choice experiment and were analyzed using conditional logit and random parameters logit models. Compared to carnosine (as a functional attribute), consumers prefer the identification of protein content. In terms of labeling carnosine, consumers have higher willingness to pay for carnosine content included in a NFT than for nutrient or health claims. Higher levels of nutrition knowledge are associated with higher willingness to pay for the different pork attributes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".