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Record W7110588710

Determination of lead in wax crayon using flame atomic absorption spectrometry / Suhailey binti Mohd Noor

2014· other· W7110588710 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Malaya Students Repository · 2014
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsWaxDetection limitOrange (colour)Atomic absorption spectroscopyOrange juiceLead (geology)
DOInot available

Abstract

fetched live from OpenAlex

The method for the determination of lead in wax crayon by Flame Atomic Absorption Spectrometry was adopted from Health Canada Safety Program (HCSP), Product Safety Laboratory, ‘Determination of Total Lead in Wax Crayon by Closed Vessel Micowave Digestion’ and verified. The parameter of method verification such as accuracy, precision, limit of detection, limit of quantitation and linearity were studied. Three colours from 10 boxes of wax crayon samples were analysed. Microwave digestion method was used to extract the lead prior to analysis. The concentration of lead in red, blue and orange colour wax crayon was 25.23 ± 3.26 mg kg-1, 21.21 ± 3.44 mg kg-1 and 32.15 ± 0.35 mg kg-1 respectively. Our results also indicate that, there is no significant different in lead content between blue and red colour wax crayon. However, there is significant different in amount of lead in orange colour compared to blue and red colour wax crayon. Previous study had estimated that a greater than 15 ug/day intake of available lead could cause a previously normal child to exceed a 10 ug/dl blood level which gave toxic effect to children.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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 designBench or experimental
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
Published2014
Admission routes1
Has abstractyes

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