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Technology and organization of black pottery production on the north coast of Peru

2019· article· en· W3092941046 on OpenAlexaff
Izumi Shimada, U. Wagner

Bibliographic record

VenueBoletín de Arqueología PUCP · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsQueen's University
FundersDeutsche ForschungsgemeinschaftNational Geographic SocietyWenner-Gren Foundation
KeywordsPotteryKilnArchaeologyPrestigeCarbon blackGeographyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Not all «black» pottery was produced in the same manner just as their social and symbolic uses and reasons for production varied a good deal. Nor are many examples truly black. The Middle Sicán culture (AD 900-1100) on the north coast of Peru distinguished itself with the perfection and large-scale production of black pottery made of fine paste. Based on our «holistic» study of a Middle Sicán workshop (including experimental firing and detailed chemical analyses of both archaeological and experimental samples), we present a detailed characterization of the blackware production technology and organization. Our study revealed that the glossy Middle Sicán blackware resulted from various factors including firing under strongly reducing conditions in small semi-closed kilns, an even carbon deposition on the vessel surface as well as penetration into the body, and the formation of graphite crystals on the well-burnished surface. Chimú reduced ware, in contrast, is typically made of coarser pastes, not as well burnished, and fired in relatively large “pit kilns” that did not permit a tight control over temperature and atmosphere. We infer that the prestige of the Middle Sicán religion and its art together with the lustrous, truly black appearance of the pottery that had been rarely achieved before played an important role in establishing the popularity of black pottery not only in the Sicán heartland but also much of the coastal Peru.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.960

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)0.0010.000

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 head0.012
GPT teacher head0.181
Teacher spread0.169 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

Explore more

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