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Record W4210361922 · doi:10.1080/02773813.2022.2033780

A note on Mössbauer analysis of white oak surfaces colored with aqueous iron salt solutions

2022· article· en· W4210361922 on OpenAlexafffund
Roberta Dagher, Tatjana Stevanović, D. H. Ryan, Véronic Landry

Bibliographic record

VenueJournal of Wood Chemistry and Technology · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsUniversité LavalMcGill UniversityNatural Sciences and Engineering Research Council of Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryAqueous solutionMössbauer spectroscopySalt (chemistry)ColoredCounterionOxidation stateInorganic chemistryMetalNuclear chemistryIonOrganic chemistryCrystallography

Abstract

fetched live from OpenAlex

Aqueous solutions of iron salts can be applied to a wood surface to modify its color. When applied on wood, iron ions are chelated by the wood’s natural phenolic compounds. The resulting color of the wood surface is due to the type of reaction products formed, such as mono-, bis- and tris- complexes of polyphenols with iron cations. In order to identify the different complexes formed on Quercus alba L. wood’s surface and the oxidation state of iron after application of different iron salts on the same wood species which influence the resulting color of wood’s surface, Mössbauer spectroscopy was performed directly on iron-stained wood samples. Colors of the stained wood samples, measured by a spectrophotometer, were analyzed in relation to the differences between the reaction products. The results showed that for a given wood species, the oxidation and reduction behavior of the iron depended on both the type of counterion and the oxidation state of the chosen iron salt.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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