MétaCan
Menu
Back to cohort
Record W3210122868 · doi:10.32920/ryerson.14661681.v1

District Energy Within the Planning Context: Exploring the Barriers and Opportunities for District Energy and Community Energy Solutions in Ontario, Canada

2021· preprint· en· W3210122868 on OpenAlexaffabout
Marlena Rogowska

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEnergy planningWork (physics)Environmental planningContext (archaeology)Government (linguistics)Land-use planningEnergy (signal processing)BusinessEnergy policyEnvironmental resource managementUrban planningLand useEnvironmental economicsEconomicsGeographyRenewable energyCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Urban land-use planning guides the way cities look and grow. Good planning leads to orderly growth and helps shape goals and policies for development while addressing important social, economic and environmental concerns. The efficiency benefits that may be garnered by linking land use planning and energy planning remain largely untapped throughout Ontario. In the case of district energy (DE), the absence of a regulatory and policy framework at the national and provincial levels results in much uncertainty regarding the associated costs and benefits of DE relative to traditional energy delivery systems. The purpose of this work is to explore Ontario’s planning framework with respect to meeting energy needs at the community level – including electrical and thermal (heating and cooling) energy needs, providing broad recommendations to all three levels of government that could help facilitate the development of district energy systems and offer more consideration to integrated community energy solutions.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.203
Teacher spread0.138 · 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 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

Citations2
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicIntegrated Energy Systems OptimizationFrench-language works237,207