AMALIA, A Matching Algorithm for Lead Isotope Analyses: Formulation and proof of concept at the Roman foundry of Fuente Spitz (Jaén, Spain)
Bibliographic record
Abstract
This article presents A Matching Algorithm for Lead Isotope Analyses (AMALIA) that yields analytical coincidences in lead isotope databases, allowing a fast selection of potential candidates for metal provenance. As a proof of concept, potential ore sources for 29 Roman lead artifacts from the archaeological site of Fuente Spitz (Jaén, Spain) are provided. Additionally, a reassessment of legacy, TIMS lead-isotope analyses is conducted by re-analysis of 26 galena samples from nearby mining districts by MC-ICP-MS. The study demonstrates the accuracy and reliability of AMALIA and stresses the need to assess the isotope ratio data obtained without lead isotopic tracers (spikes) by TIMS carefully on a case-to-case basis. At the archaeological level, our study shows that the foundries and smelting sites at Fuente Spitz and Cerro del Plomo processed galena ores from the mining districts of La Carolina and Linares to produce a variety of lead products and lead ingots that have been found at several places thorough Europe, thereby providing tangible evidence of the regional and long-distance commercial circuits that these foundries were supplying.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".