MétaCan
Menu
Back to cohort
Record W2889245371 · doi:10.1080/19440049.2018.1512760

Styrene in foods and dietary exposure estimates

2018· article· en· W2889245371 on OpenAlexafffundabout
Xu‐Liang Cao, Melissa Sparling, Luc G. Pelletier, Robert Dabeka

Bibliographic record

VenueFood Additives & Contaminants Part A · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsHealth Canada
FundersHealth CanadaCanadian Food Inspection Agency
KeywordsStyrenePopulationFood scienceBody weightPolystyreneToxicologyFood groupFood consumptionChemistryEnvironmental healthMedicineBiologyOrganic chemistryInternal medicine

Abstract

fetched live from OpenAlex

Low levels of styrene may be found in foods as a result of possible migration from polystyrene-based food packaging and as a result of its formation during the biodegradation of a wide variety of naturally occurring compounds with structures similar to styrene. In this study, composite food samples from a recent (2014) Canadian Total Diet Study were analysed for styrene, and levels of styrene in samples of most food types were low in general with a few exceptions (e.g. 4934 ng/g in herbs and spices). Dietary exposures to styrene were estimated for different age-groups based on the occurrence data and the food consumption data for all persons, and they are 0.17-0.38 µg/kg body weight/day for children and 0.12-0.16 µg/kg body weight/day for adults, similar to air intakes (0.085-0.27 µg/kg body weight/day). Thus, for the general population, both food and air contribute similar portions of the total daily intake of styrene for all age groups. However, for the smoking population, intakes from cigarettes are still the major route of exposure to styrene.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.571
Threshold uncertainty score0.863

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.307
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueFood Additives & Contaminants Part ASame topicEffects and risks of endocrine disrupting chemicalsFrench-language works237,207