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Record W4241321822 · doi:10.32920/ryerson.14643963.v1

Towards measuring real-time occupant levels to reduce ventilation fan energy consumption in existing institutional gathering spaces - a field study using thermal sensors

2021· preprint· en· W4241321822 on OpenAlexafffund
Danielle Churchill

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsSciencetech (Canada)Toronto Metropolitan UniversityMemorial University of NewfoundlandDalhousie University
FundersUniversity of Toronto
KeywordsASHRAE 90.1Ventilation (architecture)Thermal comfortArchitectural engineeringEnvironmental scienceOccupancyEnergy consumptionCurrent (fluid)Computer scienceAutomotive engineeringSimulationEngineeringMeteorologyElectrical engineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Ventilation systems in buildings have been traditionally designed for the maximum projected number of occupants; while buildings often have fewer occupants than the maximum and in some cases can be unoccupied for extended periods. Changing the rate of outdoor air to reflect changes in the number of occupants in a space is referred to as demand control ventilation (DCV). A field study was performed using thermal sensors to determine the number of occupants using lecture rooms of an institutional building. The occupant data was used to calculate minimum ventilation for the lecture rooms using current ventilation standards from ASHRAE Standard 62.1. It was found that by current standards, the required ventilation is considerably less than the original design ventilation. Based on occupant data and variables specific to the lecture rooms, it was found that the ventilation can be reduced by at least 40% creating a potential for significant energy savings.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.297
Teacher spread0.208 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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