Viability of Raman microscopy to identify micro‐residues related to tool‐use and modern contaminants on prehistoric stone artefacts
Bibliographic record
Abstract
Analyses of ancient micro‐residues and usewear 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, ancient micro‐residues are likely degraded, 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 pilot study, five stone tools from the archaeological site of Liang Bua (Flores, Indonesia) were used to evaluate the viability of Raman spectroscopy 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. Inorganic and organic deposits that occur commonly in the cave environment, including iron oxide, manganese oxide and biofilms, were identified in both the sediment and on the artefacts. Protein and saturated fatty acid micro‐residues were identified on edges of all artefacts and may partially originate from modern handling. Proteins, plant fibres and other micro‐residues associated with calcium nitrate are possibly archaeologically significant. Detection of plant fibres and starch grains may indicate either modern contamination or prehistoric contact with plant material that was transferred incidentally or during tool manufacture and/or tool use. These results demonstrate the viability of Raman microscopy to screen, at an early stage of archaeological residue analysis, for modern contaminants and micro‐residues related to tool manufacture and/or tool use. This approach serves as a base for planning strategies and analytical protocols for future work that targets larger samples of artefacts, integrates Raman microscopy with GC–MS/LC–MS and includes more comprehensive studies of usewear. Copyright © 2017 John Wiley & Sons, Ltd.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".