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Record W4248709954 · doi:10.22215/etd/2014-10948

Exploring Low-Carbon Energy Transitions in Canada: Natural Resource Staples, the Carbon Trap, and Innovating from a Hydroelectric Base

2014· dissertation· en· W4248709954 on OpenAlexaffabout
Brendan Haley

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsHydroelectricityContext (archaeology)Resource (disambiguation)ScholarshipNatural resourceSustainabilityPolitical scienceGreenhouse gasNatural resource economicsEngineeringEnvironmental resource managementEnvironmental economicsGeographyEconomicsEcologyComputer science

Abstract

fetched live from OpenAlex

This dissertation explores the broad question of how to understand and promote low-carbon transitions given Canada's historic and geographic context.An introductory essay provides commentary on the dissertation as a whole, establishes the general context for detailed research within three articles, and offers final reflections.The essay

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)0.001
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: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0120.005
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.215
Teacher spread0.197 · 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
Published2014
Admission routes2
Has abstractyes

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