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Record W2189130328 · doi:10.1139/tcsme-2009-0006

DISTRICT HEATING SYSTEM DESIGN FOR RURAL NOVA SCOTIAN COMMUNITIES USING BUILDING SIMULATION AND ENERGY USAGE DATABASES

2009· article· en· W2189130328 on OpenAlex

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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsDalhousie UniversityToronto Metropolitan UniversityHydro One (Canada)
Fundersnot available
KeywordsHeating systemNova scotiaEnvironmental scienceEnergy systemDatabaseGreenhouse gasArchetypeEnergy (signal processing)Architectural engineeringNova (rocket)Civil engineeringComputer scienceEngineeringGeographyMechanical engineeringArchaeology

Abstract

fetched live from OpenAlex

There are several benefits to district heating systems. The system design requires knowledge of community peak heating load and annual heating energy requirements. For this purpose, a residential energy model was developed using several energy usage databases. Hourly, peak, and annual heating demands were estimated by simulating 15 archetype houses using an hour-by-hour building simulation program, ENERPASS. Estimated heating profiles from model houses were used to design a district heating system for a hypothetical rural community in Nova Scotia. The findings show that building simulation is a very flexible and valuable tool in identifying the required peak and hourly energy demand of a community for the design of district energy system, and biomass district heating system can reduce community greenhouse gas emissions.

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: Methods · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.990

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 head0.032
GPT teacher head0.237
Teacher spread0.204 · 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