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Record W2593266920 · doi:10.11575/prism/30015

Keeping the Lights On: Renewable Power in Alberta's Post-Coal Era

2016· article· en· W2593266920 on OpenAlexaboutno aff
Brendan Frank

Bibliographic record

VenueOpen MIND · 2016
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyPower (physics)CoalNatural resource economicsEnvironmental scienceEngineeringEconomicsWaste managementElectrical engineering

Abstract

fetched live from OpenAlex

Alberta’s Climate Leadership Plan calls for the phase-out of coal-fired electricity in the province by 2030. Renewable power will replace two-thirds of this lost capacity, and 30 per cent of the province’s total installed and generating capacity will be provided by renewables – also by 2030. As of 2016, coal contributes 39 per cent of Alberta’s power by installed capacity and 39 by generation share, while renewables contribute 17 and 10, respectively. 1 To meet the government’s stated objectives, the province’s installed renewable capacity must grow by almost 150 per cent in just 14 years, while the generation share from renewables must grow by 400 per cent. Wind, solar, biomass (biopower), hydro, and geothermal are the candidates to replace this lost coal capacity. This paper lays out a series of quantitative metrics to test key aspects of each resource’s economic, environmental, and social feasibility in Alberta, offering comparative analysis and recommendations.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.076
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0040.002
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.291
Teacher spread0.273 · 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 designNot applicable
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

Citations0
Published2016
Admission routes1
Has abstractyes

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