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Record W1548511067 · doi:10.3130/aije.79.83

WEB-BASED QUESTIONNAIRE SURVEY ON AWARENESS AND MEASURES OF SAVING ELECTRICITY BY REGION ; TOKYO, NAGOYA AND OSAKA

2014· article· en· W1548511067 on OpenAlexaboutno aff
Sayana Tsushima, Naoe Nishihara, Shin‐ichi Tanabe

Bibliographic record

VenueJournal of Environmental Engineering (Transactions of AIJ) · 2014
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, Agriculture Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsElectricityQuestionnaireField surveyFeelingProductivityQuarter (Canadian coin)BusinessAgricultural economicsEngineeringGeographyEconomic growthPsychologyCivil engineeringEconomicsStatistics

Abstract

fetched live from OpenAlex

Some field surveys were conducted in the offices in Tokyo to investigate worker's comfort and productivity under the saving electricity after the Great East Japan Earthquake. However, we need nationwide survey because saving electricity is ongoing all over Japan now. In this study, we conducted web-based questionnaire in Tokyo, Nagoya and Osaka to know the workers' awareness and measures of saving electricity. As a result, similar saving electricity measures were implemented in three areas and lowering illuminance made less dissatisfaction than turning up the set point of the air conditioning. It is estimated that workers' awareness of saving electricity was different because of the areal targeted value of saving electricity. Workers' satisfaction of the office environment was affected to a large degree by not only indoor environment or their gender and age, but also their feelings.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.181
Teacher spread0.174 · 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 designObservational
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
Published2014
Admission routes1
Has abstractyes

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