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Record W3019134617

Survey of total mercury in infant formulae and oral electrolytes sold in Canada Part B Surveillance

2012· article· en· W3019134617 on OpenAlexaboutno aff
Robert Dabeka, Arthur D Mckenzie

Bibliographic record

VenueFood Additives and Contaminants Part A-chemistry Analysis Control Exposure & Risk Assessment · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsnot available
Fundersnot available
KeywordsMercury (programming language)DilutionInfant formulaDetection limitChemistryHealth riskContaminationElectrolyteAnimal scienceChromatographyMathematicsFood scienceMedicineEnvironmental healthPhysics
DOInot available

Abstract

fetched live from OpenAlex

Total mercury (Hg) was measured in 150 infant formula products (as sold) and oral electrolyte solutions purchased in Canada in 2003. Results less than the limit of detection (LOD) were reported as the numeric value of the LOD. Electrolytes contained the lowest concentrations, averaging 0.026 ng/g. Average levels in milk-based ready-to-use, concentrated liquid and powdered concentrate were 0.028, 0.069 and 0.212 ng/g, respectively. In soy-based formulae, the respective mean concentrations were 0.049, 0.101 and 0.237 ng/g. These concentrations cannot be considered on an absolute basis because 76% of sample concentrations fell below the limit of detection. Despite the inability to measure many of the actual background concentrations, the method was sufficiently sensitive to identify clear cases of low-level Hg contamination (up to 1.5 ng/g) of individual lots of powdered formula. Also, all the different lots of one brand of concentrated liquid infant formulae had significantly higher concentrations of Hg than those of all other concentrated liquid products. After dilution with preparation water, the Hg concentrations in all products would be lower than the Canadian Drinking Water Guideline for Hg of 1 ng/mL and too low to impact on health.

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 score0.419
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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Same venueFood Additives and Contaminants Part A-chemistry Analysis Control Exposure & Risk AssessmentSame topicWater Treatment and DisinfectionFrench-language works237,207