Mass spectrometry measurements of mercury isotope ratios support geochemical sourcing of archaeological cinnabar in the Andean region
Bibliographic record
Abstract
Geochemical methods can identify the long-distance exchange of resources in the archaeological record. Cinnabar is a mineral with a limited number of geological sources; however, methods for determining the geological origin of cinnabar are constricted by the limited availability of comparative geological source materials. This study applies a multi-method approach to compare isotopic ratios of mercury and sulfur in archaeological specimens of cinnabar from museum collections and scientifically excavated materials from the Andes region of South America. We demonstrate that the δ202Hg to Δ199Hg relationship, assessed through Multicollector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS), falls along a predictive slope, while Isotope Ratio Mass Spectrometry (IR-MS) for sulfur (S) was not a reliable proxy for determining ore source. Furthermore, Hg isotope ratios from similar sites and contexts tended to cluster, suggesting that most sites exploited cinnabar from the same ore source. Statistical analyses support the idea that the Huancavelica deposit served as the primary source of cinnabar pigment for pre-Hispanic societies, while also revealing some intriguing divergences that suggest alternate sources were exploited during certain periods on the North and South Coasts of Peru. These results demonstrate that MC-ICP-MS analyses of mercury can be used to geochemically trace cinnabar ore in the Andes and beyond.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".