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Record W1990557489 · doi:10.3992/jgb.6.4.17

EMERGENT PERMITTING STRATEGIES FOR NATURAL BUILDING SYSTEMS IN ONTARIO

2011· article· en· W1990557489 on OpenAlexaffabout
Craig Brown

Bibliographic record

VenueJournal of Green Building · 2011
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsArchitectural engineeringSustainabilityNatural (archaeology)Building envelopeOccupancyTypologyProcess (computing)Order (exchange)Built environmentEngineeringBuilding codeBuilding designEnvironmental planningDeconstruction (building)Environmental resource managementBusinessCivil engineeringComputer scienceEnvironmental scienceEcologyWaste managementGeography

Abstract

fetched live from OpenAlex

INTRODUCTION. Lowering the carbon intensity of the built environment is one of many tasks that must be undertaken in order to address climate change and to encourage sustainability. The siting, design, construction, occupancy, renovation, and disposal of single-family homes are all factors that contribute to the large carbon emissions generated by the sector. There are numerous strategies that seek to minimize the amount of emissions generated by a house during its lifecycle. This paper explores the use of so-called natural building systems in building envelope construction.Though not the silver bullet for the home industry, natural building systems are an underexplored—and underexploited—approach to home construction in Ontario. This paper will explore the barriers that this building typology faces in Ontario as well as emergent strategies for overcoming these barriers. It will be shown that within the Ontario Building Code there are numerous opportunities to make the permitting process less costly and more predictable (e.g., fewer delays). These barriers need to be eliminated if natural building systems are to emerge as a relevant strategy for lowering the carbon emissions of the residential sector.

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)0.001
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 score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.257
Teacher spread0.224 · 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

Citations1
Published2011
Admission routes2
Has abstractyes

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