MétaCan
Menu
Back to cohort
Record W2154611987 · doi:10.1520/jai100265

Determination of Equilibrium Moisture Content of Building Materials: Some Practical Difficulties

2006· article· en· W2154611987 on OpenAlexaff
MK Kumaran, Phalguni Mukhopadhyaya, Nicole Normandin

Bibliographic record

VenueJournal of ASTM International · 2006
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsEquilibrium moisture contentWater contentMaterials scienceMoistureProcess engineeringEnvironmental scienceComposite materialGeotechnical engineeringChemistryEngineeringPhysical chemistryAdsorption

Abstract

fetched live from OpenAlex

Abstract Though the definition of equilibrium moisture content of porous materials is straightforward from the point of view of equilibrium thermodynamics, experimental determination of this quantity may not always be easy. Many factors play roles from an experimentalist's point of view. Hysteresis, the reference dry state, the initial moisture content, the history of the test specimen, the size of the test specimen, the drying technique, and drying temperature—all influence the value that is arrived at by an experimentalist. This paper reports results from several series of measurements on gypsum board, eastern white pine, aerated concrete, and calcium silicate brick using the ASTM Standard C 1498 in the hygroscopic ranges and pressure plate techniques above the hygroscopic range of equilibrium moisture content. These results show the ambiguities that may surround the experimental definition of equilibrium moisture content. The results presented in the paper demand a few refinements in the existing ASTM Standard C 1498 as well as in the procedure that is used in the pressure plate method. The paper proposes a few refinements for both. The need for the development of an ASTM standard for the pressure plate technique is identified.

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 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)0.000
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 score0.010
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

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 communication0.0000.001
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 head0.024
GPT teacher head0.260
Teacher spread0.236 · 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 teacher head, 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

Citations18
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of ASTM InternationalSame topicHygrothermal properties of building materialsFrench-language works237,207