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Record W1547299765 · doi:10.1111/nyas.12779

Food safety considerations for innovative nutrition solutions

2015· article· en· W1547299765 on OpenAlexaff
Carol Byrd‐Bredbenner, Marjorie Nolan Cohn, Jeffrey M. Farber, Linda J. Harris, Tanya Roberts, Victoria Salin, Manpreet Singh, Azra Jaferi, William H. Sperber

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsHealth Canada
Fundersnot available
KeywordsFood safetyBusinessFood safety risk analysisFood packagingAgricultureFood processingFood systemsGood agricultural practicePopulationProduction (economics)MarketingRisk analysis (engineering)Food securityEnvironmental healthEngineeringMedicinePolitical scienceEconomics

Abstract

fetched live from OpenAlex

Failure to secure safe and affordable food to the growing global population leads far too often to disastrous consequences. Among specialists and other individuals, food scientists have a key responsibility to improve and use science-based tools to address risk and advise food handlers and manufacturers with best-practice recommendations. With collaboration from production agriculture, food processors, state and federal agencies, and consumers, it is critical to implement science-based strategies that address food safety and that have been evaluated for effectiveness in controlling and/or eliminating hazards. It is an open question whether future food safety concerns will shift in priority given the imperatives to supply sufficient food. This report brings together leading food safety experts to address these issues with a focus on three areas: economic, social, and policy aspects of food safety; production and postharvest technology for safe food; and innovative public communication for food safety and nutrition.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.300
GPT teacher head0.338
Teacher spread0.038 · 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 designTheoretical or conceptual
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

Citations20
Published2015
Admission routes1
Has abstractyes

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