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Record W4252756376 · doi:10.32920/ryerson.14638926.v1

A novel radiant floor system: detailed characterization and comparison with traditional radiant systems

2021· preprint· en· W4252756376 on OpenAlexafffund
Saunak Shukla, Reza Daneshazarian, Aggrey Mwesigye, Wey H. Leong, Seth B. Dworkin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRadiant heatingRadiant energyEnvironmental scienceThermalMaterials scienceNuclear engineeringMeteorologyOpticsEngineeringComposite materialRadiationPhysics

Abstract

fetched live from OpenAlex

Radiant floor systems have the potential to reduce energy consumption and the carbon footprint of buildings. This study analyzed a novel radiant panel configuration comprising a metal plate with small spikes that can be pressed into cement board or wood. The behaviour of this configuration was simulated for different materials for the metal plate, spike dimensions, and varying spacing between spikes. An annual energy simulation model compared the radiant panel configuration with traditional concrete-based system. Simulations were run under heating dominant, cooling dominant, and neutral conditions; significant cost savings and greenhouse gas emission reduction were seen across all scenarios. Keywords: Metal plate with spikes; radiant floor heating and cooling; energy efficiency; thermal comfort; computer simulation; economic optimization

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.184
Teacher spread0.164 · 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 designBench or experimental
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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