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Record W2409904253 · doi:10.21611/qirt.2014.109

Evaluation of frescoes detachments by partial least square thermography

2014· article· en· W2409904253 on OpenAlexaff
P. Bison, A. Bortolin, Gianluca Cadelano, Giovanni Ferrarini, Fernando López, Xavier Maldague

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFrescoThermographySquare (algebra)Materials scienceComputer scienceOpticsInfraredMathematicsGeometryPhysics

Abstract

fetched live from OpenAlex

Several image processing algorithms are available to enhance the results of non destructive evaluation of artworks. This work is focused on the novel Partial Least Squares Thermography (PSLT) technique, that allows a strong data compression. A preliminary test on a fresco sample and a wide survey of the San Gottardo Church (located in Asolo, Italy) have been conducted to locate detachments with an active thermography technique. All the obtained thermal sequences have been post processed with different algorithms, including Principal Component Thermography (PCT), Thermographic Signal Reconstruction (TSR) and PLST. For the latter, two different approaches have been proposed and compared.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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 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

Citations14
Published2014
Admission routes1
Has abstractyes

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