A dataset of perception and preferences of French consumers for commercial cooked hams sampled according to their nutritional values and claims.
Bibliographic record
Abstract
This dataset provides sensory and nutritional information for 30 commercial cooked hams (without rind, not flavored) representative of the French commercial segment. The sensory data were collected in two phases. During the first phase (fall 2019, field experiment), 483 consumers, regular consumers of cooked hams, were recruited in seven cities and vicinities of France. They were instructed to choose and buy cooked hams at the supermarket and evaluate them at home over a period of three months. They were provided with a list of 30 eligible cooked hams selected by the experimenters. A total of 2,758 evaluations were collected (an average of 5.7 evaluations per consumer). During the second phase (fall 2020, lab experiment), a selection of 16 cooked hams were evaluated at blind by 86 consumers in a sensory analysis laboratory using a complete balanced design. Sensory evaluation at home and in the laboratory included liking Just-About-Right (colour, fat, salt and texture) measurements. In the field experiment, consumers were additionally asked to describe with free comments the appearance, texture and flavour of tested hams and of a virtual “ideal ham”. They also had to report the price they paid for a pack of four slices of ham and their intentions to repurchase the tested hams. Other data on cooked hams included actual salt and fat contents (measured using physicochemical analyses) and information displayed on the packaging (type of brand, nutritional claims, labels).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".