New methods on the block: Taxonomic identification of archaeological bones in resin-embedded sediments through paleoproteomics
Bibliographic record
Abstract
Abstract The integration of biomolecular studies of past organisms with geoarchaeological studies can significantly improve our understanding of the relative chronology and context of archaeologically (in)visible behaviors. However, the complexity and sedimentological heterogeneity of archaeological deposits at a microscopic scale is often not taken into consideration in biomolecular studies. Here, we investigate the preservation and retrieval of paleoproteomic data from bone fragments embedded in Pleistocene resin-impregnated sediment blocks. We show that resin impregnation has minimal effect on skeletal protein taxonomic identifications in modern skeletal material but observe an increase in oxidation of methionines. We then successfully retrieve proteins from resin-impregnated blocks from the Paleolithic sites of Bacho Kiro Cave, La Ferrassie, and Quinçay. The taxonomic identifications of minute bones encased in resin are in line with previous analyses of the faunal communities of these sites, with a diversity of taxa (Bos sp./Bison sp., Equus sp., Ursus sp., and Caprinae) observed at a microscale in Bacho Kiro. This differs from results from La Ferrassie where most of the samples are identified as a single taxon (Bos sp./Bison sp.) across different areas of the site. The block from Quinçay only provided taxonomic identification of two out of eleven bone-derived samples, likely due to diagenesis. Our work indicates that paleoproteomes can be retrieved from bone fragments at a microstratigraphic resolution, enabling the detailed study of faunal community composition at a scale that more closely matches that of past human occupations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".