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Record W3042719106 · doi:10.32920/ryerson.14648811.v1

Energy Model Development and Heating Energy Investigation of the Nested Thermal Envelope Design (NTED (tm))

2021· article· en· W3042719106 on OpenAlexaboutno aff
Erin Elizabeth DIxon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsEnvelope (radar)Benchmark (surveying)ThermalWork (physics)Building envelopeEnergy (signal processing)Thermal energyOptimal designNuclear engineeringPhysicsComputer scienceMathematicsEngineeringThermodynamicsStatisticsGeologyTelecommunications

Abstract

fetched live from OpenAlex

Space heating accounts for approximately 60% of residential energy use in Canada. Minimizing envelope heat losses is one approach to reducing this percentage. Preliminary research investigated the energy-saving potential of an innovative design, referred to as Nested Thermal Envelope Design (NTED(TM)). The concept involves one insulated building inside another with dual thermal zones. Conservative modeling results from this work showed heating energy reductions of 74%. This research developed a new NTED(TM) simulation model to provide increased accuracy and gain a more complete understanding of the potential heating energy savings. The working performance was also investigated by modeling occupied-building operation. The resulting model has shown that the NTED(TM) design yields savings of 85% relative to a benchmark R-2000 building. These results improve on the preliminary values and reinforce the merit of the design as a means of achieving significant reductions in residential energy use.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.171
Teacher spread0.153 · 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
Published2021
Admission routes1
Has abstractyes

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