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Record W4410336603 · doi:10.2471/b09327

Phasing out coal-fired electric power generation - implications for public health Canada

2025· book· en· W4410336603 on OpenAlexfundaboutno aff

Bibliographic record

VenueWorld Health Organization eBooks · 2025
Typebook
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
FundersHealth CanadaGovernment of the United KingdomWorld Health Organization
KeywordsCoalElectricity generationPower (physics)Political scienceEnvironmental scienceEngineeringWaste managementPhysics

Abstract

fetched live from OpenAlex

Case study: Phasing out coal-fired electric power generation in Canada Phasing out coal-fired electric power generationimplications for public health Canada: a success story Key messageCanadian federal and provincial governments have effectively implemented policies to address greenhouse gas (GHG) emissions from the combustion of fossil fuel for electric power generation (EPG), particularly through the policy of eliminating conventional coal-fired EPG units in Canada by 2030.Such policies have the additional benefit of greatly reducing air pollution emissions from this sector, resulting in billions of dollars of population health benefits, which can help to offset the costs of climate change mitigation. WHO Air Quality, Energy and Health Science and Policy Summaries Key definitions Electric power generation (EPG):The process of producing electricity to power households, industries and transit systems.It involves converting various energy sources, such as fossil fuels (coal, natural gas), nuclear and renewables (wind, solar, hydro), into electrical energy. Greenhouse gas (GHG):Gases emitted from the combustion of fossil fuels (e.g.coal) and other processes that trap heat in the atmosphere, contributing to global warming and climate change.Common GHGs include carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O).

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.530
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.035
GPT teacher head0.264
Teacher spread0.229 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes2
Has abstractyes

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