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Record W2238536013 · doi:10.1177/0003702815616595

Combined Spectroscopic Analysis of Beads from the Tombs of Kindoki, Lower Congo Province (Democratic Republic of the Congo)

2016· article· en· W2238536013 on OpenAlexfundno aff
Anastasia Rousaki, Alessia Coccato, Charlotte Verhaeghe, Bernard Clist, Koen Bostoen, Peter Vandenabeele, Luc Moëns

Bibliographic record

VenueApplied Spectroscopy · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsnot available
FundersBijzonder Onderzoeksfonds UGentUniversiteit GentParks Canada
KeywordsBeadRaman spectroscopyArchaeologyChemometricsElemental analysisAssemblage (archaeology)Archaeological scienceSpectroscopyMaterials scienceGeologyAnalytical Chemistry (journal)ChemistryGeographyOpticsEnvironmental chemistryPhysics

Abstract

fetched live from OpenAlex

Raman spectroscopy and X-ray fluorescence (XRF) analysis are commonly applied to archaeological objects as a fast and nondestructive way to characterize the materials. Here, micro-Raman spectroscopy and chemometrics on handheld XRF results were used to completely characterize beads found during archaeological excavations in the Congo. Metallic objects, organogenic materials, and glass beads were studied. Special attention was paid to the glassy materials, as they seem to be of European production. The matrix family and crystalline phases assemblage, as well as the results from principal components analysis on the elemental data, were used to define groups of beads of similar composition, and therefore probably of similar origin. This research project establishes the feasibility of this approach to archaeological glasses, and can be used to confirm and support the bead typologies used by archaeologists.

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 score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.210
Teacher spread0.197 · 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

Citations34
Published2016
Admission routes1
Has abstractyes

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