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Record W2931818762 · doi:10.3390/coatings9040225

Coating Performance on Exterior Oil-Heat Treated Wood

2019· article· en· W2931818762 on OpenAlexafffundabout
Mojgan Nejad, Mahdi Dadbin, Paul Cooper

Bibliographic record

VenueCoatings · 2019
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Toronto
FundersMitacs
KeywordsGloss (optics)WeatheringCoatingMoistureWater contentPinus <genus>Environmental sciencePulp and paper industryPine woodComposite materialMaterials scienceHorticultureBotanyGeologyGeotechnical engineeringBiology

Abstract

fetched live from OpenAlex

Thermal modification and the degree of improved properties from the treatment depend on wood species and treatment parameters. Southern yellow pine and spruce are two wood species commonly used for decking, fences, and siding in North America. This study evaluated coating performance when applied on oil-heat-treated Southern pine and spruce wood samples. Moisture content, color, and gloss changes of samples were analyzed before weathering and then after each month for the first three months and then every six months during 18 months of natural weathering exposure in Toronto, Canada. The results showed that coated heat-treated woods had lower moisture uptake, lower color change, and overall better appearance ranking than coated-untreated wood samples. Coated-spruce wood samples had lower checking and splitting, and in general, much better performance than coated-Southern pine treated samples. Notably, the average moisture content of treated spruce wood samples was significantly lower than that of Southern pine, which explains lower checking and improved coatings’ appearance.

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 categoriesInsufficient payload (model declined to judge)
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 score0.203
Threshold uncertainty score1.000

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)0.0000.001

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 head0.010
GPT teacher head0.183
Teacher spread0.173 · 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.

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

Citations12
Published2019
Admission routes3
Has abstractyes

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