MétaCan
Menu
Back to cohort
Record W4303648887 · doi:10.2307/j.ctv2z9fzr0

The Elemental Analysis of Glass Beads

2022· book· en· W4303648887 on OpenAlexfundaboutno aff

Bibliographic record

VenueUniversitaire Pers Leuven eBooks · 2022
Typebook
Languageen
FieldEngineering
TopicStructural Analysis of Composite Materials
Canadian institutionsnot available
FundersKU LeuvenUniversity of TorontoAgence Nationale de la RechercheGraduate Women in ScienceWilfrid Laurier UniversityTrent UniversityNational Geographic SocietyNational Science Foundation
KeywordsBeadMaterials scienceElemental analysisComposite materialChemistryInorganic chemistry

Abstract

fetched live from OpenAlex

Ancient glass beads as a window to the ancient world Glass beads, both beautiful and portable, have been produced and traded globally for thousands of years. Modern archaeologists study these artifacts through sophisticated methods that analyze the glass composition, a process which can be utilized to trace bead usage through time and across regions. This book publishes open-access compositional data obtained from laser ablation – inductively coupled plasma – mass spectrometry, from a single analytical laboratory, providing a uniquely comparative data set. The geographic range includes studies of beads produced in Europe and traded widely across North America and beads from South and Southeast Asia traded around the Indian Ocean and beyond. The contributors provide new insight on the timing of interregional interactions, technologies of bead production and patterns of trade and exchange, using glass beads as a window to the past. This volume will be a key reference for glass researchers, archaeologists, and any scholars interested in material culture and exchange; it provides a wide range of case studies in the investigation and interpretation of glass bead composition, production and exchange since ancient times. Contributors: Bernard Gratuze (Institut de Recherche sur les ArchéoMATériaux, Centre Ernest-Babelon, UMR 5060 CNRS/Université d'Orléans), Alicia L. Hawkins (University of Toronto Mississauga), Elliot H. Blair (University of Alabama), Jessica Dalton-Carriger (Roane State Community College), Lee M. Panich (Santa Clara University), Thomas R. Fenn (The University of Oklahoma), Alison K. Carter (University of Oregon), Jennifer Craig (McGill University), Mark Aldenderfer (University of California, Merced), Mudit Trivedi (Stanford University), Lindsey Trombetta (The University of Texas at Austin), Jonathan R. Walz (The Field Museum / SIT-Graduate Institute), Akshay Sarathi (Florida Atlantic University), Carla Klehm (University of Arkansas), Marilee Wood (University of the Witwatersrand), Katherine A. Larson (Corning Museum of Glass), Heather Walder (The Field Museum / University of Wisconsin – La Crosse), Laure Dussubieux (The Field Museum) Supplementary Material 'The Elemental Analysis of Glass Beads' Ebook available in Open Access. This publication is GPRC-labeled (Guaranteed Peer-Reviewed Content).

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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.005
GPT teacher head0.179
Teacher spread0.174 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueUniversitaire Pers Leuven eBooksSame topicStructural Analysis of Composite MaterialsFrench-language works237,207