Whole-rock trace-element analyses applied to the regional sourcing of ancient basalt vessels from Egypt and Jordan
Bibliographic record
Abstract
"Fingerprinting" lithic artefacts using whole-sample geochemistry is a simple, inexpensive, technique that can supply archaeologists with important provenance and trade information. To demonstrate its utility, it is applied here to basalt vessels produced by Near East societies encompassing the millennia and geographic areas where civilization arose and writing developed. Using published whole-sample geochemical data for bedrock samples, exploratory statistical techniques show that Jordanian and Egyptian basalts are fundamentally distinct. Petrogenetically significant plots (VTi) and element ratios (Rb/Sr, Nb/Y, Sr/Zr) efficiently "fingerprint" and separate Jordanian and Egyptian bedrock basalt samples and Levantine and Egyptian basaltic artefacts. The results show that most basalt artefacts were manufactured and used within the geographic regions and culture areas where they were produced. However, a representative sample of some typologically distinct basaltic artefacts from Maadi, Egypt, geochemically resembles Palestinian basalts and quantitatively confirms archaeological evidence that trade interactions between Egyptian and Jordanian Neolithic societies were established early. Thus, knowledge of the bedrock source of raw materials used in the manufacture of basaltic artefacts is useful for inferring trade and social interaction between and within these cultures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".