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Record W2110195535 · doi:10.1145/2470654.2466153

Everyday activities and energy consumption

2013· article· en· W2110195535 on OpenAlexafffund
Carman Neustaedter, Lyn Bartram, Aaron Mah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConsumption (sociology)Energy consumptionArtifact (error)Computer scienceEnergy (signal processing)Everyday lifePower consumptionEnvironmental economicsPower (physics)EngineeringSociologyArtificial intelligencePolitical scienceEconomics

Abstract

fetched live from OpenAlex

Energy consumption is a growing concern and it is important to inform families of their consumption and how they might reduce it. We conducted an interview study that focuses on the existing routines of families and how they currently understand their power and gas consumption based on standard utility bills. We also investigated how this understanding ties to their everyday activities as might be recorded on their calendars. This allowed us to assess calendars as an artifact for energy consumption awareness. Our results show that many people relate changes in energy consumption to high-level effects such as weather and temperature and not necessarily their own everyday activities. Events on calendars may aid this understanding but people do not currently record enough information on their calendars to make a strong tie. This suggests that if calendars are to be used as artifacts to aid energy consumption understanding, digital calendars need to provide support to include more energy-related information, including both activities and patterns of consumption.

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)0.004
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 score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.006
GPT teacher head0.210
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

Citations32
Published2013
Admission routes2
Has abstractyes

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