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Record W2528620333 · doi:10.1108/ohi-03-2008-b0004

Implementation Strategies for Solar Communities

2008· article· en· W2528620333 on OpenAlexaffabout
Cassidy Johnson, L. Dignard‐Bailey

Bibliographic record

VenueOpen House International · 2008
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsGovernment of CanadaNatural Resources Canada
Fundersnot available
KeywordsRenewable energySubsidyScale (ratio)Work (physics)BusinessSolar energyProcess (computing)Environmental economicsArchitectural engineeringConsumption (sociology)EngineeringEnvironmental planningEnvironmental resource managementPolitical scienceGeographyEnvironmental scienceEconomicsSociologyComputer science

Abstract

fetched live from OpenAlex

Work on the design and implementation for solar homes has been expanded to the community scale in several international projects. If low-carbon emission housing is to make an impact on citywide consumption of energy, we must move towards community-scale implementation of solar technologies, both in new housing developments and in existing ones. However, the uptake of solar communities requires new methods for implementation to promote innovation in the building industry, new policies and programmes on energy consumption and energy subsidies, as well as community-scale design guidelines for solar or other renewable technologies. This research surveys the implementation process of selected solar community projects in Netherlands, United States and Canada. It looks at new policies and programmes that are promoting community-scale solar projects from the perspective of innovation in the building industry. It examines the various actors that are necessary for solar communities within an urban planning framework and identifies five main actor groups.

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)0.010
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0050.003
Scholarly communication0.0050.004
Open science0.0020.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0210.001

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 head0.379
GPT teacher head0.486
Teacher spread0.107 · 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

Citations6
Published2008
Admission routes2
Has abstractyes

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