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Record W4308173060 · doi:10.46873/2300-3960.1358

Addressing specific safety and occupational health challenges for the Canadian mines located in remote areas where extreme weather conditions dominate

2022· article· en· W4308173060 on OpenAlexafffundabout
Rachid Halabi, Mustafa Kumral

Bibliographic record

VenueJournal of Sustainable Mining · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsMcGill UniversityGovernment of Northwest Territories
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMine safetyExtreme weatherCertificationLegislatureOccupational safety and healthBusinessEnvironmental planningEngineeringEnvironmental resource managementGeographyEnvironmental sciencePolitical scienceClimate changeCoal miningLaw

Abstract

fetched live from OpenAlex

The number of mining operations is increasing in the Canadian North, where extreme weather conditions govern. Currently, many mine development projects are also in progress in this region. These mines’ working atmosphere and employment circumstances are highly different from regular mines. One of the main differences is the special safety issues of the Canadian North. The primary sources of these special issues are: the difficulty of finding skilled employees; high employee turnover rate; insufficient training and certification requirements; delicate employment circumstances affecting the psychological well-being of employees; permafrost; mine inspection challenges; inventory and logistic hardship; and the legislative and regulative necessities corresponding to the particular working environment. This paper aims to set forth specific safety cases in the mines located in the Canadian North. Then, it argues the characteristics of safety organizations and management required to deal with these cases. Furthermore, how the current frameworks can be improved is discussed. Safety issues stemming from cold weather conditions and location remoteness of mines add further challenges to the viability and implementation of projects. The paper underlines that mining operations need certain safety organizations, management approaches, and specific regulations for the mines operated in remote areas and under severe weather conditions.

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)0.003
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.394

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0220.005
Scholarly communication0.0050.001
Open science0.0020.004
Research integrity0.0030.002
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 head0.212
GPT teacher head0.447
Teacher spread0.235 · 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

Citations6
Published2022
Admission routes3
Has abstractyes

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