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Record W2113873672 · doi:10.5772/56127

Risk Assessment of Exposure to Trans Fat in Canada

2012· article· en· W2113873672 on OpenAlexafffundabout
Sara Krenosky, Mary LAbb, Nora Lee, Lynne Underhill, Michel Vigneault, Samuel Benrejeb Godefroy, Nimal Ratnayake

Bibliographic record

VenueInternational Food Risk Analysis Journal · 2012
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsHealth Canada
FundersHealth CanadaKorea Electrotechnology Research Institute
KeywordsRisk assessmentEnvironmental healthMedicineComputer scienceComputer security

Abstract

fetched live from OpenAlex

Trans fats are undesirable because they raise LDL-cholesterol and lower HDL-cholesterol levels in the blood, which can lead to an increased risk of coronary heart disease. In the mid-1990’s, researchers estimated that Canadians had one of the highest average trans fat intakes in the world, estimated to be approximately 3.7% of energy. The World Health Organization recommends that average intakes of trans fats should be less than 1% of total energy. As such Canada has pursued a multi-faceted approach to decrease trans fat levels in Canadian foods. Initiatives undertaken include: mandatory nutrition labelling, the establishment of a multi-stakeholder Trans Fat Task Force to develop recommendations and strategies to eliminate trans fat in Canadian foods, and most recently the monitoring of industry’s efforts in reducing trans fats from their food products. Collectively, these initiatives have proven successful as average trans fat intakes have been reduced to 1.42% of overall energy. Further reductions in trans fat levels in the Canadian food supply are needed to meet the target of 1% of energy, the associated public health objectives, and the protection of vulnerable populations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.343
Teacher spread0.324 · 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 designObservational
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

Citations10
Published2012
Admission routes3
Has abstractyes

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