A NOVEL APPROACH TO STUDIES OF PREHISTORIC EXPLOITATION OF STONE TOOL MATERIALS USING MATERIAL COMPOSITION, SURFACE MORPHOLOGY, MICROSTRUCTURE AND MECHANICAL PROPERTIES*
Bibliographic record
Abstract
For a comprehensive understanding of material exploitation in prehistory, we applied advanced analytical methods to Japanese prehistoric stone tool materials. Compositions, surface morphologies, microstructures and mechanical properties of the primary lithic materials were analysed. As a result of the tests on actual Palaeolithic artefacts, preferential material selection was observed based on composition, structure and other physical properties of the materials. Homogeneous materials composed of a single type of mineral—α‐quartz—were intentionally selected for Palaeolithic tools regardless of the type of rock. These materials unexceptionally present higher hardness and strength. Moreover, materials composed of extremely fine crystal grains of ~0.1 µm in size with highest hardness and strength were selectively used for sharp‐edged blades. These results lead us to the conclusion that quantitative and objective analyses will give us precise information on prehistoric materials, which will enable us to make an analytical approach to the comprehension of prehistoric exploitation of stone materials. This could eventually complement the traditional interpretation of material exploitation based on conventional petrological classifications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".