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Record W4311398790 · doi:10.1007/s41982-022-00133-9

Characterising the stone artefact raw materials at Liang Bua, Indonesia

2022· article· en· W4311398790 on OpenAlexafffund
Sam Lin, Lloyd T. White, Jatmiko Jatmiko, I Made Agus Julianto, Matthew W. Tocheri, Thomas Sutikna

Bibliographic record

VenueJournal of Paleolithic Archaeology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsLakehead University
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of WollongongUniversity of New EnglandLeakey FoundationWaitt FoundationNational Geographic SocietyAustralian Research CouncilSmithsonian Institution
KeywordsGeologyStone toolDiagenesisArchaeologyGeochemistryMineralogyMineralGeographyMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Abstract At Liang Bua, the type site of Homo floresiensis on the Indonesian island of Flores, the stone artefact assemblages are dominated by two raw materials, qualitatively classified as chert and silicified tuff in previous studies. Field observations describe both stone types as locally abundant and of good flaking quality, but no systematic analysis has yet been carried out to characterise their nature. In this study, we conducted the first geological, mechanical, and quantitative assessment of these two raw materials using a suite of analytical approaches. Our results show that the two stone types are mineralogically alike in composition and derive from fossiliferous limestone that had undergone diagenetic silica replacement, but they clearly differ from one another geochemically. Therefore, the ‘chert’ and ‘silicified tuff’ categories used in previous studies are more aptly described as silica-dominated (i.e., SiO 2 -dominated) nodular chert and iron-rich (i.e., Fe 2 O 3 -rich) nodular chert, respectively. We discuss the implications of our results on the shift in raw material utilisation patterns at Liang Bua that occurred after ~ 46 ka and coincided with the arrival of Homo sapiens at the site.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.294
Teacher spread0.270 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations3
Published2022
Admission routes2
Has abstractyes

Explore more

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