Observations and questions on gold artefacts from underwater excavations
Bibliographic record
Abstract
In the present paper, we will study examples of settlement excavations that can sometimes reveal abundant gold material. These -underwater excavations took place in Alexandria and Aboukir Bay, Egypt, where we found numerous gold or gold-plated objects. Of these, some objects provide us with information about the trade routes used in those times, and others show us that refinement existed even during the Byzantine period. Some objects tell us fabulous stories, while others attest a high mastery of the available technology.Through specific examples, we will approach the scientific, aesthetic, historical and technological aspects pertaining to these gold objects.While most of these gold objects, jewellery and coins, have been studied before, and the results published elsewhere, considering the number of excavated objects, it is typically the less spectacular ones or the ones that were too difficult to interpret that have been neglected.Being an archaeologist specialised in the restoration of metal, I am thus not a metallurgist, a technologist, or even a numismatist, nor a historian specialised in the production or trade of gold objects. However, on the boat, underwater, or in the land laboratory, many questions concerning these archaeological objects deserve attention.With the help of three Case studies, we will see that some problems related to these objects have come up, and that a close collaboration with various gold artefact specialists appears necessary in order to try to solve these uncertainties.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".