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Record W4401631908 · doi:10.22215/etd/2024-16150

Development of a Hygrothermal Model Validation Methodology Using a Two-Storey Guarded Hot Box

2024· dissertation· en· W4401631908 on OpenAlexaff
Robin Frances Hilbrecht

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsCarleton University
Fundersnot available
KeywordsModel validationEnvelope (radar)Building envelopeExperimental dataEngineeringSensitivity (control systems)Component (thermodynamics)Computer scienceStructural engineeringSimulationReliability engineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

Hygrothermal performance is an essential component of building performance, with heat, air, and moisture transport through the building envelope as a key element.Current modelling programs such as Delphin can be used to simulate hygrothermal performance of building envelopes but require physical data for validation, either from in-situ or controlled laboratory experiments.This allows for confidence in the use of hygrothermal models.This thesis used a two-storey guarded hot box (GHB) apparatus to develop a testing sequence that provided sufficient data for confidence in validation.Two lightweight vertical walls were tested in the GHB, each for 1200 hours.The model validation was completed using graphical and statistical analysis.After an initial validation, model adjustments were completed based on the sensitivity of various model parameters.The improved models resulted in excellent agreement between the experimental and modelled performance.

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)0.003
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.096
GPT teacher head0.324
Teacher spread0.228 · 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
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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