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Record W1893894820 · doi:10.1039/b618400f

Multi-component assessment of worker exposures in a copper refinery : Part 2. Biological exposure indices for copper, nickel and cobalt

2007· article· en· W1893894820 on OpenAlexaff
Evert Nieboer, Yngvar Thomassen, Natalya Romanova, Alexander NIKONOV, Valery Chaschin

Bibliographic record

VenueJournal of Environmental Monitoring · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCopperNickelRefineryCobaltComponent (thermodynamics)Environmental scienceEnvironmental chemistryMetallurgyChemistryMaterials scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Urinary copper (Cu), nickel (Ni) and cobalt (Co) concentrations were determined for 127 Cu refinery workers (40 females, 87 males), with values of the 95% upper confidence interval of the geometric mean in nmol per mmol creatinine of 89 (Ni), 42 (Cu) and 3.4 (Co) for electrorefinery workers. In the pyrometallurgical departments, the corresponding concentrations were 37 (Ni), 99 (Cu) and 11 (Co). Female workers had higher Co urinary concentrations than males (p< or = 0.05) while no gender difference was observed for Cu and Ni. Inter-elemental correlations were moderate to weak. Based on the inhalable aerosol levels reported previously for the same workers, the observed urinary Cu concentrations were considerably lower than expected, relative to Co and Ni. This is interpreted in terms of the current understanding of Cu homeostasis.

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)0.001
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

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

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 head0.038
GPT teacher head0.306
Teacher spread0.267 · 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

Citations30
Published2007
Admission routes1
Has abstractyes

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