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Record W2048091761 · doi:10.1177/1071181311551082

Energy Management in Large Enterprises

2011· article· en· W2048091761 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2011
Typearticle
Languageen
FieldEnergy
TopicEnergy Efficiency and Management
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsEnergy managementEnergy (signal processing)Work (physics)Knowledge managementEnergy consumptionField (mathematics)Computer scienceBusinessProcess managementRisk analysis (engineering)Engineering

Abstract

fetched live from OpenAlex

Energy managers are responsible for controlling business energy consumption. Wider adoption of energy management behavior would enable more efficient energy systems; however the costs of current energy management tools, tasks and training are beyond many small enterprises. Efforts to support more widespread energy management would benefit from better understanding the expert cognitive work performed at large enterprises. A preliminary study of this work was conducted by interview and participant observation. Energy managers apply skill in energy analysis, business management, and agreeable communication to cost-effectively monitor and report business-relevant energy findings. These activities are complicated by uncertainty in energy data and variability in business structure, which introduce tradeoffs between costs of social data interpretation and maintenance of complex datasets and models. To help clarify debate on appropriate design of energy management tools, tasks, and training, we propose four human-centered research topics of cues, trust, strategies, and sensors.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.215
Teacher spread0.199 · 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