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Record W2114085819 · doi:10.1177/1097196304042117

A Comparison of Different Techniques to Quantify Moisture Content Profiles in Porous Building Materials

2004· article· en· W2114085819 on OpenAlexaff
Staf Roels, Jan Carmeliet, Hugo Hens, O.C.G. Adan, H.J.P. Brocken, Robert Černý, Zbyšek Pavlík, Armin T. Ellis, Christopher Hall, Kumar Kumaran, L. Pel, Rudolf Plagge

Bibliographic record

VenueJournal of Thermal Envelope and Building Science · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsWater contentMeasure (data warehouse)Thermal diffusivityMaterials scienceReliability (semiconductor)MoisturePorosityAttenuationWork (physics)Porous mediumProcess engineeringEnvironmental scienceBuilding materialComposite materialComputer scienceGeotechnical engineeringMechanical engineeringEngineeringOpticsThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Several advanced non-destructive techniques are available to measure the evolution of content profiles with time, allowing the analysis of unsaturated flow and the determination of the moisture diffusivity of porous building materials. The reliability of six different techniques is investigated: the NMR-technique, the MRItechnique, the γ-ray attenuation technique, the capacitance method, the X-ray projection method and the TDR-technique. All of them were applied to measure the moisture content evolution during free uptake experiments on two building materials. Considering the limitations of some of the techniques, a good overall agreement is obtained. The work presented is an outcome of the EU-initiated HAMSTAD-project.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.031
GPT teacher head0.307
Teacher spread0.276 · 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

Citations124
Published2004
Admission routes1
Has abstractyes

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