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Food Safety Challenges within North American Free Trade Agreement (NAFTA) Partners

2011· article· en· W2148829884 on OpenAlexaffabout
Richard A. Holley

Bibliographic record

VenueComprehensive Reviews in Food Science and Food Safety · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFood safetyBusinessCredibilityRationalization (economics)Product (mathematics)SafeguardingPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Abstract: Organizational elements and limitations influencing the effective operation of the food safety regulatory infrastructures in the United States, Canada, and Mexico are compared. Progress to improve the safety of food in North American countries is hampered by common problems, yet differences exist. Foodborne illness surveillance and reporting are most comprehensive in the United States, but it is uniformly more reactive than proactive in all 3 countries. Food safety policy is based on outbreak data, but that may be short‐sighted because these represent roughly 10% of foodborne illness cases. Food inspection in each country is done at 2 tiers (federal and other) by many agencies at 3 (federal/state‐provincial/municipal) levels. Interagency collaboration at times of crisis is weak and frequent heterogeneity in training, inspection targets, and inspection rigor affect regulatory credibility. Enhanced recognition that industry has the prime responsibility for food safety is warranted (and must not be confused with self‐inspection) along with justifiably aggressive regulatory agency interrogation of food safety system performance. End product testing should be used to verify safety system operation and should not be used to predict product safety. Specific microbial and nonmicrobial challenges to safe food in North America are highlighted and a rationalization of fiscal/human resource allocation to most effectively reduce the burden of foodborne illness is provided.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.139
GPT teacher head0.282
Teacher spread0.143 · 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.

Study designOther design
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

Citations15
Published2011
Admission routes2
Has abstractyes

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