The Hidden Treasures: The Use of Computed Tomography to Study Metal Objects in Funerary Bundles from the Central Coast of Peru
Bibliographic record
Abstract
Abstract This work presents a methodological alternative to the traditional study of objects arranged inside funerary bundles, with the aim of preserving the integrity of the bundle and optimizing the material resources and costs derived from the storage and unwrapping processes. The research employs computerized tomographic scans to study the metal artifacts chosen to accompany the individuals arranged inside funerary bundles. It is the first systematic characterization of funerary treatment to focus on the presence of metal objects as part of burial offerings and their relationship with the body of the deceased individuals in the Andean area. Analyzing a total of 85 funerary bundles from the central coast of Peru, the study identified 26 bundles, dating to between AD 1100 and 1532, that contained at least one metal object. The objects were recorded to identify their use, decoration, measurements, location within the bundle, and the presence of any other objects associated with the individual, which made it possible to discern metal objects present in bundles corresponding to female, male, and nonadult individuals. The research concludes that the presence of metal artifacts in a funerary bundle is an indicator of elevated status, although the choice of specific artifacts is determined by elements of an individual’s identity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".