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Record W4392918532 · doi:10.1111/maps.14160

Does the composition of meteoritic metal change with forging? An experimental study

2024· article· en· W4392918532 on OpenAlexafffund
Mendy M. Ouzillou, C. D. K. Herd

Bibliographic record

VenueMeteoritics and Planetary Science · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMeteoriteForgingTrace elementMetallurgyKamaciteGeologyBronzeElectron microprobeMaterials scienceChondriteAstrobiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.235
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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