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Record W3081694166 · doi:10.5539/ies.v13n9p123

Green University Using Cloud Based Internet of Things Model for Energy Saving

2020· article· en· W3081694166 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldComputer Science
TopicInternet of Things and AI
Canadian institutionsnot available
Fundersnot available
KeywordsCloud computingThe InternetSample (material)Computer scienceInternet of ThingsProcess (computing)World Wide Web

Abstract

fetched live from OpenAlex

The research was conducted to study and develop a Green University using Cloud based Internet of Things model for energy saving. The aims of this study were 1) to study and design 2) to evaluate a model of Green University using Cloud based Internet of Things for energy saving. There are two phases of the research method. The first phase included the model design: 1) to study, analyze, and synthesize the contents, 2) to develop a process model of Green University using Cloud based Internet of Things, 3) to present the constructed model, and 4) to conclude the results. The second phase is referred to an evaluation of the model. Nine experts from Green University’s Electrical, Information Technology, and university management team were included in the research as sample group. Then, the data were analyzed by standard deviations and means. The model development process has 3 components that include 10 procedures. This model helps to energy saving. As the overall model was shown at a very good level, the experts agreed.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.353

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 science0.0010.000
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 head0.083
GPT teacher head0.318
Teacher spread0.235 · 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