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Record W2146669971 · doi:10.1177/1097196304042436

Assessment Method of Numerical Prediction Models for Combined Heat, Air and Moisture Transfer in Building Components: Benchmarks for One-dimensional Cases

2004· article· en· W2146669971 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Thermal Envelope and Building Science · 2004
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBenchmark (surveying)MoistureWork (physics)Heat transferEnvironmental scienceComputer scienceEngineeringMechanical engineeringMeteorologyMechanicsGeologyPhysics

Abstract

fetched live from OpenAlex

The standardised Glaser method for calculation, prediction and evaluation of moisture performance is considered as rarely applicable. The present state of knowledge, analytical as well as experimental, concerning heat, air and moisture demands updating of standards. This paper presents five numerical benchmark cases for the quality assessment of simulation models for one-dimensional heat, air and moisture (HAM) transfer. In one case, the analytical solution is known and excellent agreement between several solutions from different universities and institutes is obtained. In the remaining four cases, consensus solutions have been found, with good agreement between different HAM models. The work presented here is an outcome of the EU-initiated project for standardisation of HAM calculation methods (HAMSTAD WP2).

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.026
GPT teacher head0.277
Teacher spread0.252 · 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