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Record W4323922664 · doi:10.1515/hf-2022-0189

Wood color modification with iron salts aqueous solutions: effect on wood grain contrast and surface roughness

2023· article· en· W4323922664 on OpenAlexafffund
Roberta Dagher, Tatjana Stevanović, Véronic Landry

Bibliographic record

VenueHolzforschung · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsUniversité LavalNatural Sciences and Engineering Research Council of Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAqueous solutionSurface roughnessSurface finishStainChemistryMaterials sciencePorosityWettingMineralogyComposite materialStainingOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Wood is a biosourced material with unique aesthetic features due to its anatomy and chemical composition. White oak wood surface color can be modified with the use of iron salts, which react with wood phenolic extractives, present as free molecules in wood porous structure. The impact of modifying wood surface color with iron salts on the final appearance of wood, including its color, grain contrast and surface roughness, was evaluated in this study. Results showed that following the application of iron (III) sulphate aqueous solutions on white oak wood surface, its roughness increased, which is due to grain raising after wetting of wood surface. The color modification of wood surface with iron (III) sulphate aqueous solutions was compared with a non-reactive water based blue stain. The contrast associated to wood grain that was expressed by the standard deviation of luminance values in wood images, also increased after application of the iron (III) sulphate aqueous solution on white oak wood surface. The comparison of contrast changes showed that wood samples stained with iron (III) sulphate on their curved surface had the highest increase in grain contrast compared to iron-stained wood showing the straight grain and to wood surfaces colored by a non-reactive water-based stain for both curved and straight grains.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.639

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 studies0.0010.000
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 head0.035
GPT teacher head0.240
Teacher spread0.206 · 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 designTheoretical or conceptual
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

Citations11
Published2023
Admission routes2
Has abstractyes

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