Using X‐ray fluorescence to examine ancient Maya granite ground stone in Belize
Bibliographic record
Abstract
Abstract While ubiquitous among ancient Maya sites in Mesoamerica, archaeological analysts frequently overlook the interpretive potential of ground stone tools. The ancient Maya often made these heavy, bulky tools of coarse‐grained, heterogeneous materials that are difficult to chemically source, unlike obsidian. This paper describes an application of handheld, energy‐dispersive X‐ray fluorescence (XRF) to provenance ground stone artifacts (tools and architectural blocks) composed of granite: a nonhomogenous, phaneritic stone. We present a multicomponent methodology that independently tested whole‐rock, thin‐sectioned, and powdered samples by petrographic microscope, conventional, lab‐based XRF, and portable XRF units, which yielded comparable results. After establishing distinct geochemical signatures for the three geographically restricted granite plutons in Belize, we devised a field‐based XRF application on a whole rock that could replicate the compositional readings of lab‐based XRF on powdered materials with sufficient accuracy and reliability. We applied this multishot XRF technique to granite ground stone items from a range of ancient Maya sites throughout Belize; we discuss two specific case studies herein. Our results underscore the widespread potential of multishot XRF applications for determining the provenance of coarse‐grained, heterogeneous rock materials. These results can help push the boundaries from one‐dimensional, functional explanations of ground stone items to their social and ideological dimensions, alongside deeper understandings of granite resource management.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".