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Record W2437806659 · doi:10.3130/aijt.22.645

FACTOR ANALYSIS ON ELECTRICITY CONSERVATION RATES IN WELFARE FACILITIES

2016· article· en· W2437806659 on OpenAlexaff
Sayaka KINDAICHI, Daisaku NISHINA, Saburo Murakawa, Shigeki Kametani, Hiroto Takaguchi, Hisashi Hanzawa, Hiroshi Yoshino, Yoshiharu ASANO, Masaya Okumiya, Yoshiyuki Shimoda, Hirotoshi Yoda, Yuzo Sakamoto, Kazuaki Bogaki, Shuzo Murakami

Bibliographic record

VenueAIJ Journal of Technology and Design · 2016
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, Agriculture Analysis
Canadian institutionsDepartment of Environment and Conservation
FundersTohoku UniversityNagoya UniversityWaseda UniversityOsaka University
KeywordsElectricityConsumption (sociology)WelfareEnergy conservationEnergy consumptionFunction (biology)BusinessEnvironmental scienceEnvironmental economicsAgricultural economicsEngineeringEconomicsElectrical engineeringMarket economy

Abstract

fetched live from OpenAlex

Changes of the energy consumption in welfare facilities before and after the Great Earthquake of East Japan were analyzed based on the investigation for development of the DECC. Both electricity and primary energy consumption in the facilities with the residential function were reduced by approximately 8% in the Tohoku region and 16% in the Kanto region in 2011 compared to those in 2010 even though the difference of air temperatures were taken into consideration. As the results from multivariate analyses, measures related to lighting were effective for energy savings in the facilities both with and without the residential function. Additionally, changes of setting temperature of freezers could be a strong factor for energy savings in the facilities with the residential function, whereas restriction of use of electric appliances such as portable electric pots had large influences in the facilities without the function.

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)0.005
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.216
Teacher spread0.204 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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