Raman Microscopy as a Primary Technique for Identifying Micro-residues Related to Tool-use on Prehistoric Stone Artefacts
Bibliographic record
Abstract
Analyses of ancient micro-residues preserved on stone artefacts can potentially provide detailed information about how prehistoric humans used the artefacts to process materials such as food, pigments and/or adhesives. However, prehistoric micro-residues are likely to degrade and there are multiple potential sources of contamination, such as contact with sediments, groundwater, recent handling, storage materials or laboratory conditions, any of which can inhibit reliable identification of micro-residues and other traces of prehistoric use. In this chapter we illustrate the use of Raman spectroscopy as a primary method to identity ancient micro-residues preserved on stone artefact surfaces that are due specifically to prehistoric use as opposed to some form of ancient or modern source of contamination. Stone tools from Liang Bua (Flores, Indonesia) and Denisova Cave (Altai Mountains, Siberia) are used to demonstrate the methodology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".