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Record W7027563584

CSF Building Energy Consumption Analysis and Cost Estimate of Electric Resistive Heating System

2023· other· en· W7027563584 on OpenAlexafffundabout

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2023
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of Newfoundland
KeywordsEnergy consumptionProcurementInvestment (military)SustainabilityConsumption (sociology)Electric energyGreenhouse gasHeating systemEnergy (signal processing)Electric heating
DOInot available

Abstract

fetched live from OpenAlex

This paper presents findings from an energy consumption analysis of the Core Science Facility (CSF) at Memorial University of Newfoundland (MUN) and estimates the cost of implementing an electric resistive heating system. The study aims to assess current energy usage based on twelve months of actual consumption data and evaluate the feasibility of transitioning to an energy-efficient heating system. The analysis indicates the current Energy Use Intensity (EUI) is around 2.15GJ/m2, compared to the National median reference of 1.04GJ/m2 for a university. \nThe cost estimate includes upfront investment for procurement and installation, with consideration of operational costs. Findings will be used to develop an energy model using Energy Plus Open Studio software to explore the potential savings while reducing greenhouse gas emissions. The study highlights the importance of environmental sustainability and long-term benefits of energy-efficiency in alignment with sustainability goals.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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 designSimulation or modeling
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
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueMemorial University Research Repository (Memorial University)Same topicNuclear Structure and FunctionFrench-language works237,207