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Record W4297474320 · doi:10.3390/molecules27196378

Violin Varnishes: Microstructure and Nanomechanical Analysis

2022· article· en· W4297474320 on OpenAlex
Marianne Odlyha, Jeannette Jacqueline Łucejko, Anna Lluveras‐Tenorio, Francesca Di Girolamo, Stephen Hudziak, Adam Strange, Alexandra Bridarolli, Laurent Bozec, Maria Perla Colombini

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMolecules · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVarnishViolinMaterials scienceNanoscopic scaleComposite materialSynchrotron radiationAtomic force microscopyMicrostructureCharacterization (materials science)NanotechnologyOpticsArtPhysicsArt historyCoating

Abstract

fetched live from OpenAlex

The aim of the current work is twofold: to demonstrate the application of in situ non-invasive imaging by portable atomic force microscopy (AFM) on the surfaces of a violin and to integrate compositional and mechanical analysis at the nano scale level on model samples of varnished wood. These samples were prepared according to traditional recipes by an Italian lute-maker family well practised in the art. Samples of oil and spirit-based varnishes on maple wood, naturally and accelerated light aged, were studied. AFM was used to measure the nanomechanical properties of the model samples and established that the spirit-based varnish was stiffer than the oil-based. Synchrotron radiation micro- Fourier Transform Infra-red analysis of the layer structure revealed that stiffer spirit-based varnish showed less penetration into the wood than the oil-based. Further PeakForce Quantitative Nanomechanical Mapping (QNM) demonstrated a difference in adhesion values between the oil- and spirit-based samples.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score0.993

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)0.0080.000

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 head0.036
GPT teacher head0.203
Teacher spread0.167 · 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