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Record W4395059544 · doi:10.29173/alr2683

Pathways to Net-Zero: Opportunities for Canada in a Changing Energy Sector

2021· article· en· W4395059544 on OpenAlexvenueaboutno aff
Brendan Downey, Mike Henry, Robyn Finley, Sean Korney, John L. Zhou

Bibliographic record

VenueAlberta Law Review · 2021
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsZero (linguistics)Energy sectorNet (polyhedron)Net energyBusinessEnergy (signal processing)EconomicsNatural resource economicsMathematicsStatistics

Abstract

fetched live from OpenAlex

The climate is changing, and Canada is changing with it. Canada has committed to reducing its greenhouse gas emissions. Initiatives have been taken, but more work is needed. Private enterprise is key to the invention, improvement, and proliferation of sustainable energy sources. The extent to which the Canadian economy can be decarbonized hinges in part on how effectively regulatory schemes facilitate and incentivize commercial endeavours to exploit low-carbon energy sources. This article accordingly evaluates foreseeable regulatory frameworks for three new lower-carbon energy sources: hydrogen, geothermal energy, and biofuels. The thread running throughout this article is that, while there are many challenges ahead, there is also opportunity: regulatory schemes are evolving and adapting to support business ventures that monetize these three renewable energy sources.

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)0.006
metaresearch head score (Gemma)0.011
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 score0.099
Threshold uncertainty score0.717

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0100.008
Scholarly communication0.0110.004
Open science0.0030.003
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0110.001

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 head0.052
GPT teacher head0.271
Teacher spread0.219 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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