Re-approaching palaeodiet in the Andes: use and application of sulphur isotope analysis in reconstructing Peruvian palaeodiet
Bibliographic record
Abstract
This research critically examines palaeodietary analyses in ancient Peru. Research is often approached using ceramics, flora, and faunal remains to examine human diet and behaviour prior to written records however these remains may not be indicative of items used exclusively for subsistence. More directed approaches employ stable isotope analyses of human remains as these data can provide direct indication of foods consumed during life. Peruvian isotope studies focus on 13C-enrichment patterns, and follow the premise that maize (corn) was the main source of 13C-enrichment recorded in bone collagen. Recent studies in Peru have identified other dietary sources that cause similar enrichment patterns, including kiwicha (pseudocereal), marine protein (e.g. shellfish, fish, and seals), and mococho (seaweed). As a result, additional methodologies must be employed to more sufficiently identify sources of subsistence in ancient Peru. I propose that stable sulphur isotope methodology may be used to overcome the issues presented. By critically reviewing previous palaeodietary analyses of Peru I examine current limitations and overview the application potential of carbon and nitrogen isotope studies complemented with sulphur isotope analysis of human and faunal remains within a Peruvian context. Ultimately I advocate for a more comprehensive approach to Peruvian palaeodiet.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".