Characterising the stone artefact raw materials at Liang Bua, Indonesia
Bibliographic record
Abstract
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., SiO2-dominated) nodular chert and iron-rich (i.e., Fe2O3-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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".