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Record W2980501078 · doi:10.1364/oe.27.031338

Standoff Raman spectroscopy for architectural interiors from 3-15 m distances

2019· article· en· W2980501078 on OpenAlexfundno aff
Yu Li, Chi Shing Cheung, Sotiria Kogou, Florence Liggins, Haïda Liang

Bibliographic record

VenueOptics Express · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilNational Lottery Heritage FundSight Research UKNatural Environment Research CouncilTrent UniversityNottingham Trent University
KeywordsOpticsRaman spectroscopySpectroscopyMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Portable and mobile Raman spectroscopy systems are increasingly being adopted in in situ non-invasive examination of artworks given their high specificity in material identification. However, these systems typically operate within centimeter range working distances, making the examination of large architectural interiors such as wall paintings in churches challenging. We demonstrate the first standoff Raman spectroscopy system for in situ investigation of historic architectural interior at distances > 3 m. The 780 nm continuous wave laser-induced standoff Raman system was successfully deployed for the in situ examination of wall paintings, at distances of 3-15 m, under ambient light. It is able to identify most common pigments while maintaining a very low laser intensity to avoid light induced degradation. It is shown to complement our current method of standoff remote surveys of wall paintings using spectral imaging.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.236
Teacher spread0.217 · 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

Citations21
Published2019
Admission routes1
Has abstractyes

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