Label perception of frozen ready-to-eat products and frozen not-ready-to-eat product
Bibliographic record
Abstract

 Background: Frozen meals are popularized in recent years due to their ease of preparation. This convenience factor greatly benefits busy workers who simply lack the time to cook a full meal. However, the risk of misidentifying these frozen products as cooked when they are in fact, raw, can lead to devastating consequences. This is important especially when the products are improperly prepared and undercooked. Some significant examples in recent years includes the Salmonella cases associated with frozen raw breaded chicken. These cases are partly due to the inadequate cooking of the product, as a result of misidentifying them as cooked even though they are raw. The purpose of this project is to determine how well the public can determine if a frozen product is cooked or raw based on the front side of the packaging, which is the first visuals that will be presented to the consumers in store. Methods: An electronic survey was conducted for Canadian residents to determine whether they can accurately interpret if a product is cooked or raw based on the front packaging. The survey also determines if the respondent’s age, gender, average number of supermarket visits in a week, and level of education will affect the accuracy of their interpretations. The survey was created and hosted online with SurveyMonkey, and distributed out in Reddit. The results are analyzed using the statistical software, NCSS 12. Results: Chi-square tests indicated no significant difference between the demographics groups and the accuracy of the label interpretations by the respondents. Five different products; chicken pot pie, fish fillets, breaded chicken wings, poutine bites, and tourtiere pie, were chosen for identification, each with their own label statements, respectively; “cook thoroughly”, “uncooked”, “fully cooked”, “heat thoroughly” and one with no label statement. The fish fillets, poutine bites and the tourtiere pie had the most varied answers from the respondents. The poutine bites and tourtiere pie had the majority of the respondents selecting the wrong answer or being unsure. The fish fillets had the majority choosing the correct answer, but given the simplicity of the label “uncooked”, it was surprising that only 45% of the respondents chose “require additional cooking”. Additionally, a few of the open ended comments from respondents indicate some desire for labels clarity in regards to fonts and color on the packaging, as well as having clear, standardized statements that clearly identifies the products as cooked or raw. However, there are some comments that indicate the current labels are adequate, and some comments mentioning about labelling on the back of the box. Conclusion: Based on the results of the study, it would appear that the demographic groups selected have no effect on the accuracy of label identifications of frozen products. The study also indicates that there is preference from the public to favours clear and straightforward labelling statements. The study identifies potential problems with some ambiguity in the label statements (or lack of label statements), and some potential issues with the noticeability of the statements to the consumers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".