A geoarchaeological methodology for sourcing chert artefacts in the Mediterranean region: A case study from Neolithic Skorba on Malta
Bibliographic record
Abstract
Abstract This article introduces a robust scientific methodological approach that has been effective on accurately sourcing prehistoric chert artefacts. The research focuses on the lithic assemblage of Skorba, a late Neolithic site of Malta, and local chert rock sources. This assemblage is mainly consisting of chert tools and artefacts, but the origin of the raw materials remains inconclusive. Although chert outcrops are reported on Malta, they have yet to be investigated and their petrological characteristics are unknown. Moreover, it was always assumed that nonlocal chert material has been only imported from Sicily. This, however, remains at a theoretical level and elaborate provenance research is necessary to test it. This archaeological background serves an excellent opportunity to employ an interdisciplinary methodology and address uncertainties that conventional archaeological practices seem unable to provide clear answers. This methodology includes geological techniques that focus on petrological and geochemical characteristics of chert formations. The collected results provide the necessary scientific evidence to connect some artefacts with their actual sources and provided useful information about the possible origin of others. This paper further aims to demonstrate the great prospects of this suite of techniques and its suitability for similar provenance studies of chert material worldwide.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".