Lead isotopes and archaeometallurgy
Bibliographic record
Abstract
This special issue presents several studies applying lead isotope analysis (LIA) to address archaeological questions in the field of ancient mining and metallurgy.They were presented during an international workshop, held in Fribourg (Switzerland), June 19-20, 2008, organised in collaboration between the Geosciences Department of the University of Fribourg and the Laboratory of Prehistoric Archaeology and Human Peopling (Department of Anthropology and Ecology) of the University of Geneva with the financial support of the Swiss National Science Foundation.Dr. F. Cattin and Dr. B. Guénette-Beck were in charge of the scientific organization.More than 30 scientists from Belgium, France, Germany, Italy and Switzerland attended the sessions with the seven key lectures dedicated to different aspects of actual research in the field of LIA.Over the last few years, several studies of archaeological artefacts using lead isotope analysis have shown a renewed interest in this technique.The workshop aimed to bring together experienced senior researchers and young scientists active in the field to promote contacts and discussions.The idea was to address research questions on four general points:1. Analytical considerations: possibilities, advantages and shortcomings of various methods like TIMS, ICP-MS and LA-ICP-MS Since the first application of Pb isotopic data in archaeology, its acquisition has relied on different analytical methods.From this concern, it is legitimate to ask if one lead isotope analysis is equivalent to another lead isotope analysis.First of all, thermal ionisation mass spectrometry (TIMS) was the only method used in Pb isotope measurement until the introduction of high precision multi-collector inductively coupled plasma mass spectrometer, as detailed in the LIA historical perspective of Stos-Gale and Gale.The more recent development of mass spectrometry with laser ablation reduced the sample size and therefore widened new perspectives of applications, with the opportunity to investigate small or precious artefacts and micro-scale sampling in a heterogeneous matrix.The methodological paper of Villa discusses the protocols of all these techniques, with a focus on the one processed at the Laboratory of Isotope Geology at the University of Berne (Switzerland).What comes out of this contribution is that accuracy should concentrate our full attention when acquiring new data and that precision, whatever high or not, may allow distinction between what needs to be differentiated regarding specific archaeological questions.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".