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Evaluation of the Thermal Resilience of a Community Hub

2022· article· en· W4297497437 on OpenAlexaboutno aff
Aylin Ozkan, Joel Good

Bibliographic record

VenueASHRAE/IBPSA-USA Building Simulation Conference · 2022
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsSurvivabilityResilience (materials science)Baseline (sea)Duration (music)Building envelopeComputer scienceArchitectural engineeringEnvironmental scienceCertificationReliability engineeringEngineeringMeteorologyThermal

Abstract

fetched live from OpenAlex

The frequency of power outages as a result of climate pressures are increasing and community centres are relied on as areas of refuge. Therefore, designing these buildings with passive survivability in mind is important, and challanging due to its high occupancy and fresh air requirements. This paper aims to evaluate the design measures of a community hub in the Canadian province of Ontario through the thermal resilience metric of passive survivability using building performance simulations. This study compares the performance of design strategies from building certification programs commonly followed to achieve high-performance envelope design. These include Passive House, Zero Carbon Building and Ontario Building Code (OBC) compliance criteria. The performance indicator is defined as the duration of time that the buildings remain habitable until the arrival of help or repair in the event of an extended power outage. Using whole-building energy modeling a power outage is simulated and the building is forced to rely on passive means of maintaining comfort, the PH and ZCB cases show far improved thermal autonomy (60% and 49% acceptable, respectively) when compared to the code baseline (18%). The high-performance envelopes of the PH and ZCB cases also result in greater passive survivability performance in both extreme summer and winter conditions.

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)0.002
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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.290
Teacher spread0.238 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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