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Record W2781011035 · doi:10.1080/19338244.2017.1409692

Silica exposure in a mining exploration operation

2017· article· en· W2781011035 on OpenAlexaffabout
Victoria H Arrandale, Sheila Kalengé, Paul A. Demers

Bibliographic record

VenueArchives of Environmental & Occupational Health · 2017
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsCancer Care OntarioPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsOccupational exposureEnvironmental scienceExposure assessmentMining engineeringWaste managementEnvironmental healthEngineeringMedicine

Abstract

fetched live from OpenAlex

Background: Though there is extensive research on occupational exposure in production mines, there is limited information on exposure during the exploration phase of mining.Methods: Air samples were collected in a core processing facility in Northern Ontario, Canada. All samples were analyzed for respirable dust (NIOSH 0600) and respirable crystalline silica (NIOSH 7602). Mean exposure levels were estimated and differences in exposure between work areas were investigated.Results: Sixteen personal and nine area air samples were collected. Respirable dust exposure ranged from < LOD to 2.24mg-m−3; respirable silica exposure ranged from < LOD to 0.055mg-m−3. Silica concentrations were higher among workers in the core cutting and core sorting (pulp and reject) areas, as compared to those in the core logging area.Conclusions: Workers employed in core processing facilities as part of mining exploration activities are exposed to respirable silica; exposure controls may be needed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

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

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 head0.032
GPT teacher head0.326
Teacher spread0.293 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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