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Record W2331963465 · doi:10.1080/15459624.2013.843784

Respirable Dust and Respirable Silica Exposure in Ontario Gold Mines

2013· article· en· W2331963465 on OpenAlexaffabout
Dave K. Verma, Gyan S. Rajhans, Om P. Malik, Karen des Tombe

Bibliographic record

VenueJournal of Occupational and Environmental Hygiene · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsMcMaster University Medical CentreSGS (Canada)McMaster UniversityResearch CanadaHamilton Health Sciences
Fundersnot available
KeywordsEnvironmental scienceOccupational exposureWaste managementMining engineeringEnvironmental chemistryEnvironmental healthEngineeringChemistryMedicine

Abstract

fetched live from OpenAlex

A comprehensive survey of respirable dust and respirable silica in Ontario gold mines was conducted by the Ontario Ministry of Labor during 1978-1979. The aim was to assess the feasibility of introducing gravimetric sampling to replace the assessment method which used konimeters, a device which gave results in terms of number of particles per cubic centimeter (ppcc) of air. The study involved both laboratory and field assessments. The field assessment involved measurement of airborne respirable dust and respirable silica at all eight operating gold mines of the time. This article describes the details of the field assessment. A total of 288 long-term (7-8 hr) personal respirable dust air samples were collected from seven occupational categories in eight gold mines. The respirable silica (α-quartz) was determined by x-ray diffraction method. The results show that during 1978-1979, the industry wide mean respirable dust was about 1 mg/m(3), and the mean respirable silica was 0.08 mg/m(3.)The mean% silica in respirable dust was 7.5%. The data set would be useful in future epidemiological and health studies, as well as in assessment of workers' compensation claims for occupational diseases such as silicosis, chronic obstructive pulmonary disease (COPD), and autoimmune diseases such as renal disease and rheumatoid arthritis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.226
Teacher spread0.206 · 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.

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

Citations29
Published2013
Admission routes2
Has abstractyes

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