Tracking trachyte on the Roman routes: Provenance study of Roman infrastructure and insights into ancient trades in northern Italy
Bibliographic record
Abstract
Abstract Roman colonization of northern Italy during the late Republican Age brought about significant building activity in the newly acquired territories, involving the construction of new infrastructure that demanded large amounts of stone. Trachyte of the Euganean Hills was among the most commonly used materials for building roads, bridges, forum squares, and aqueducts. This paper addresses the recognition of the provenance quarry of Euganean trachyte used in Roman public infrastructure in northeastern Italy. Petrographic features and major‐ and trace‐element composition of bulk rock and phenocrysts, analyzed by X‐ray fluorescence (XRF) and laser ablation inductively coupled plasma mass spectrometry (LA‐ICPMS), were used as provenance tracers. The provenance determinations allow for exploring the commercial, political, and economic dynamics involving the supply of trachyte for public works, and the management of Roman quarries, which likely were in competition with each other and separately controlled by the most important nearby cities. Finally, broad insights into ancient trades in northern Italy and the main routes of stone distribution are discussed: most transport was done by ship, being more rapid and less costly, taking advantage of the Adriatic Sea, the Po River, and the many waterways close to the Euganean quarries.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".