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Record W4385901176 · doi:10.4324/9781003280118-19

Planning a Low-Carbon City in a High-Carbon State

2023· book-chapter· en· W4385901176 on OpenAlexaboutno aff
Sonak Patel, John R. Parkins

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon fibersState (computer science)Environmental scienceBusinessComputer scienceAlgorithm

Abstract

fetched live from OpenAlex

Renewable energy development is critical in the Province of Alberta, where approximately 90 per cent of electricity generated is produced by fossil fuels. Despite the timely need to introduce more renewable energy to meet carbon-reduction targets, Alberta has seen limited and slow renewable development. Environmental motivations include reducing carbon emissions by replacing carbon-intensive energy generation. Community energy projects represent innovations that differ from the dominant method of energy generation, yet may present solutions to the failures of the existing regime, including the carbon intensity and lack of energy democracy. Edmonton is pursuing energy-transition efforts, with actions to reduce energy demand, use energy more efficiently, and replace carbon intensive energy with renewable sources, including the development of local energy projects within city limits. The response of the municipality’s citizenry is of key importance to municipal decision-makers, as citizens can interrupt unwelcome municipal action and set directives through municipal elections.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.457
Threshold uncertainty score0.908

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.002

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 head0.172
GPT teacher head0.356
Teacher spread0.184 · 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 designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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