Does the composition of meteoritic metal change with forging? An experimental study
Bibliographic record
Abstract
Abstract The use of meteoritic iron in the manufacture of human artifacts since the Bronze Age has been well documented, including the iron blade of Tutankhamun's dagger. Whereas the preservation of textures and mineral inclusions suggest relatively low temperature (<950°C) working of meteoritic metal used in artifacts, higher temperature working—that is, forging—could have occurred, based on studies of Bronze Age slag. The extent to which the forging of meteoritic iron might change the bulk composition, especially the trace elements used for classification of iron meteorites, is largely unknown. Using electron microbeam methods (SEM and EPMA), and trace element analysis (ICP‐MS), we analyze metal obtained at different stages during the modern forging of a set of knife blades from fragments of the Gebel Kamil meteorite, and assess the degree to which bulk element composition, mineral inclusions, and textures are modified. We find that while forging does destroy the original texture and removes mineral inclusions, it does not significantly modify the trace elements typically used in iron meteorite classification, at least for the relatively Ni‐rich composition represented by Gebel Kamil. While we acknowledge that the modern method by which the knife blades were forged from Gebel Kamil would not have occurred in the Bronze Age, our results represent an upper temperature limit relative to the inferred conditions used in ancient forging. The identification of the meteorite (if still in existence) that was used for artifacts is feasible, based on our results and current literature on ancient meteoritic artifacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".