Combined organic biomarker and use-wear analyses of stone artefacts from Liang Bua, Flores, Indonesia
Bibliographic record
Abstract
Organic biomarker and lithic use-wear analyses of archaeological implements manufactured and/or used by hominins in the past offers a means of assessing how prehistoric peoples utilised natural resources. Currently, most studies focus on one of these techniques, rather than using both in sequence. This study aims to assess the potential of combining both methods to analyse stone artefacts, using a set of 69 stones excavated from the cave site of Liang Bua (Flores, Indonesia). Prior to chemical analysis, an initial inspection of the artefacts revealed potential use-wear traces but no visible residues. Gas chromatography mass spectrometry (GC-MS) analysis, including the targeting of 86 lipids, terpenes, terpenoids, alkanes and their analogues, found compounds with plant or animal origin on 27 of the 69 stones. The artefacts were subsequently cleaned, and use-wear analysis identified traces of use on 43 artefacts. Use-wear analysis confirmed traces of use on 23 of the 27 artefacts with potential use-residues that were determined by GC-MS. The GC-MS results were broadly consistent with the functional classes identified in the later use-wear analysis. This inclusive approach for stone artefact analysis strengthens the identifications made through multiple lines of enquiry. There remain conflicts and uncertainties in specific cases, suggesting the need for further refinement and analyses of the relationships between use-wear and residues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.012 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".