MétaCan
Menu
Back to cohort

Determining the impact of sensor orientation on moisture content measurements in eastern white pine

2021· article· en· W4200221672 on OpenAlexaff
B Daouk, Christopher Baldwin, Cynthia A. Cruickshank

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsCarleton University
Fundersnot available
KeywordsWater contentElectrodeMaterials scienceMoistureComposite materialEnvironmental sciencePinus <genus>Orientation (vector space)Geotechnical engineeringGeologyBotanyMathematicsChemistryGeometry

Abstract

fetched live from OpenAlex

Abstract As buildings become more airtight and insulated, the movement and accumulation of moisture within building envelopes become paramount in determining its resiliency. Current methods for quantifying the moisture content (MC) of wood species involve the measurement of electrical resistance between two installed electrodes and the use of existing empirical correlations to evaluate the MC. However, these correlations do not adequately consider the impact of sensor orientation within wall assemblies. The objective of this paper is to determine the impact of MC readings within a wood sample due to sensor orientation. A total of 126 eastern white pine samples were tested with electrodes placed along the grain of the wood (longitudinal), across the grain of the wood (tangential), and in a diamond pattern, using six different fasteners as electrodes. The samples were placed in a controlled environmental chamber until steady state was achieved at approximately 18% MC. Electrical resistances of the samples were measured in both directions at temperatures ranging from -10°C to 40°C. It was found that the tangential-to-longitudinal resistance ratio is 1.1-1.35 depending on the electrode type.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.264

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 communication0.0000.000
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 head0.065
GPT teacher head0.277
Teacher spread0.212 · 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 designObservational
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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicSmart Materials for ConstructionFrench-language works237,207