The natural variability of clay and its impact on provenance study of pottery in Vanuatu and further afield
Bibliographic record
Abstract
Abstract This article documents the significant horizontal (across the landscape) and vertical (across the stratigraphy) chemical variability of volcanic clays from Vanuatu, South Pacific. Data illustrate why the chemical composition of the clay matrix in pottery should be used very cautiously in characterization or provenance studies. The variability of natural clays on Efate, Erromango, and Malekula is so significant that the data set disproves the assumption that two pottery samples with clay matrices showing similar chemical composition necessarily originate from the same location, the same bedrock, the same region, or even from the same island. This study is also a reminder that the outcomes of chemical characterizations and provenance studies of pottery are directly dependent on the scale at which the investigation is undertaken. In light of the data, it is also clear that such studies should not be undertaken in other similar insular environments affected by regular volcanic activity along the Circum‐Pacific Belt without assessing the natural variability of the raw material. Without an adequate sampling of natural clay representative of the vertical and geographic variability, the results from the chemical analysis of clay matrices risk of leading to incorrect associations between pots and procurement areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".