MétaCan
Menu
Back to cohort
Record W2078583777 · doi:10.1504/ijetm.2011.039276

A model for assessment of energy utilisation within an urban centre

2011· article· en· W2078583777 on OpenAlex
Poornima Jayasinghe, Anil K. Mehrotra, J. Patrick A. Hettiaratchi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Environmental Technology and Management · 2011
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsExergyEnvironmental scienceElectricityGreenhouse gasEnergy analysisEnvironmental engineeringCoalEnergy (signal processing)Waste managementEngineering

Abstract

fetched live from OpenAlex

This paper presents development of a model for analysis of energy and exergy utilisation within an urban centre of Calgary, Canada. The analysis began with the detailed assessment of energy resource utilisation patterns and energy flows across six different energy consuming sectors, namely energy generation, residential, commercial, industrial, transportation and agricultural. For each sector, the energy and exergy efficiencies were determined. Calgary's overall energy and exergy efficiencies were found to be 40.9% and 15.7%, respectively. Thereafter, the developed model was used to identify energy and exergy losses in different sectors and potential areas for improvement. It was determined that, by switching coal with natural gas by 50%, the CO2 emissions can be reduced by 24.3%. In addition, as much as 31.5% reduction in emissions is also possible by reducing the electricity usage up to 50%.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.526
Threshold uncertainty score0.236

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.213
Teacher spread0.198 · 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