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Record W3154971306 · doi:10.3390/min11040436

Down to the Crust: Chemical and Mineralogical Analysis of Ceramic Surface Encrustations on Bronze Age Ceramics from Békés 103, Eastern Hungary

2021· article· en· W3154971306 on OpenAlexaff
Mark Golitko, Alyssa McGrath, Attila Kreiter, Ian V. Lightcap, Paul R. Duffy, Györgyi Parditka, Julia I. Giblin

Bibliographic record

VenueMinerals · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of Toronto
FundersNational Science Foundation
KeywordsCalciteGeologyLeaching (pedology)MineralogyDiagenesisCeramicChemical compositionGeochemistryWeatheringBronze AgeArchaeologyMetallurgyMaterials scienceChemistrySoil water

Abstract

fetched live from OpenAlex

Békés 103, a primarily Middle Bronze Age (c. 1600–1280 calBC) cemetery and settlement on the Great Hungarian Plain, has been investigated by the BAKOTA project since 2011. Ceramics from the site are covered in dense white concretions, and it has been noted during compositional analyses that these vessels exhibit elevated concentrations of several potentially mobile elements in comparison to vessels from regional tell sites. Here, we use a multimethod (optical mineralogy, FT-IR, XRD, XPS, PXRF, SEM-EDS, and LA-ICP-MS) mineralogical and chemical approach to characterize the composition of surface encrustations on ceramics samples from Békés 103. We also chemically map interior paste composition using LA-ICP-MS to identify potential leaching of mobile elements into or out of vessel bodies. We demonstrate that the surface encrustations are primarily composed of calcite but also contain a variety of other mineral and organic constituents indicative of deposition of soil carbonates, phosphates, nitrates, and other inorganic and organic components. We further document the leaching of several mobile elements into ceramic pastes as well as formation of secondary calcite along void, pore, and temper boundaries. The presence of cremated bone and possibly bone ash in close vicinity to many of the studied vessels may also have contributed to the observed patterns of diagenesis. It is likely that similar post-burial processes might affect ceramics from other sites located in low-lying, seasonally inundated contexts.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.225
Teacher spread0.206 · 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.

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

Citations11
Published2021
Admission routes1
Has abstractyes

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