MétaCan
Menu
Back to cohort
Record W2101768710 · doi:10.1002/gea.1019

The major‐ and trace‐element whole‐rock fingerprints of Egyptian basalts and the provenance of Egyptian artefacts

2001· article· en· W2101768710 on OpenAlexaff
John D. Greenough, M. P. Gorton, Leanne M. Mallory‐Greenough

Bibliographic record

VenueGeoarchaeology · 2001
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of TorontoOkanagan University College
Fundersnot available
KeywordsBedrockProvenanceBasaltTrace elementGeologyMineralGeochemistryMineralogyGeomorphologyMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Abstract Discrimination diagrams have been developed that source Egyptian basaltic artefacts using whole‐rock major element geochemistry. These include K 2 O versus SiO 2 , TiO 2 and P 2 O 5 against MgO/Fe 2 O 3 t (total Fe as Fe 2 O 3 ), and a discriminant analysis diagram using SiO 2 , Fe 2 O 3 t , CaO, and MnO. A complementary set of diagrams uses easily obtained trace element data (Nb/Y versus Zr/Nb; Zr [ppm] versus Rb/Sr; TiO 2 [wt % volatile free] versus V; and Cr [ppm] versus Zr/Y) to determine the bedrock sources. These diagrams have been applied to seven First Dynasty basalt vessels (Abydos), two Fourth Dynasty basalt paving stones (Khufu's funerary temple, Giza), and two Fifth Dynasty paving stones (Sahure's complex, Abu Sir). They show that the bedrock source for all the artefacts was the Haddadin flow in northern Egypt. Multidimensional scaling and cluster analysis applied to the whole‐rock data (major elements and trace elements together) and previously published mineral fingerprinting studies confirm these results. Comparing mineral versus whole‐rock fingerprinting techniques, a major advantage of the former is the small sample size required (0.001 g compared to ≥ 0.1 g). Analytical costs are similar for both methods assuming that a comparison (bedrock) database can be assembled from the literature. For most archaeological problems, a whole‐rock bedrock database is more likely to exist than a mineral database, and whole‐rock analyses on artefacts will generally be easier to obtain than mineral analyses. Whole‐rock fingerprinting may be more sensitive than mineral‐based fingerprinting. Thus, if sample quantity is not an issue, whole‐rock analysis may have a slight cost, convenience, and technical advantage over mineral‐based methods. Our results also emphasize that the Egyptians cherished their Haddadin basalt flow and used it extensively and exclusively for manufacturing basalt vessels and paving stones for at least 600 years (∼3150 B.C. to 2500 B.C., approximate ages of the vessels and Abu Sir paving stones, respectively). © 2001 John Wiley & Sons, Inc.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueGeoarchaeologySame topicGeochemistry and Geologic MappingFrench-language works237,207