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Record W2598612477 · doi:10.47339/ephj.2014.151

Frozen foods and recommended packaging temperatures

2014· article· en· W2598612477 on OpenAlexvenueno aff
Evan Sudiono, Environmental Health BCIT School of Health Sciences, Bobby Sidhu, Helen Heacock, Lorraine McIntyre

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsFood scienceProduct (mathematics)Food productsBusinessCooked meatMathematicsChemistry

Abstract

fetched live from OpenAlex


 Background: Frozen foods have cooking instructions on their packaging, but due to foodborne illnesses resulting from consuming them, it brings the effectiveness of these instructions into question. The recommended cooking temperature on the packaging is a specific numerical value that is not open to interpretation and can be used to measure effectiveness. Methods: Temperatures were taken from 208 different meat products from different stores. The information recorded include: the store the products were found at, the type of meat, whether the product was uncooked or cooked, and if it had safe handling instructions. The data was compared to 3 different guidelines to see if they met the recommendations or not. The results of the comparison were then analyzed using Chi-squared tests. Results: A majority of T&T products failed in all 3 standards, the majority of products from Superstore passed using all 3 standards, and the majority of products from Costco failed using 2 standards. Conclusion: The amount of products that met recommendations is dependent on the store, the type of meat, the uncooked or cooked status, and the guidelines being used due to the recommended temperature of poultry being vastly different in one of the guidelines. The other products that did not meet recommendations were due to them being cooked products without a recommended reheating.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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 designNot applicable
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

Citations0
Published2014
Admission routes1
Has abstractyes

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