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The Essence of Smart Homes

2016· book-chapter· en· W2489543997 on OpenAlexaff
Amirhosein Ghaffarianhoseini, Ali GhaffarianHoseini, John Tookey, Hossein Omrany, Anthony Fleury, Nicola Naismith, Mahdiar GhaffarianHoseini

Bibliographic record

VenueAdvances in media, entertainment and the arts (AMEA) book series · 2016
Typebook-chapter
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsKey (lock)Home automationArchitectural engineeringSmart citySmart environmentQuality (philosophy)Computer securityBusinessEngineeringComputer scienceInternet privacyRisk analysis (engineering)Internet of ThingsTelecommunications

Abstract

fetched live from OpenAlex

Smart homes have been predominantly pointed as one of the key constituents of intelligent environments. These are residential units substantially integrated with a communicating network of sensors and intelligent systems based on the application of new design initiatives and creative technologies. This study provides a holistic overview on the essence of smart homes besides demonstrating their current status, benefits and future directions. The study reveals that smart homes embrace significant potentials towards achieving comfort, security, independent lifestyle and enhanced quality of life. Findings urge the necessity to focus on further exploration of the social and environmental benefits derived from the application of creative technologies in smart homes. The study concludes that smart homes play a fundamental role in shaping the future cities. Finally, the study identifies a research gap indicating that there has been less consideration towards linking the fundamental potentials of smart homes to the overall performance and key indicators of smart cities.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0050.006
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations9
Published2016
Admission routes1
Has abstractyes

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