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Record W3046094422 · doi:10.1017/aaq.2020.46

Detecting Early Widespread Metal Use in the Eastern North American Arctic around AD 500–1300

2020· article· en· W3046094422 on OpenAlexaff
Patrick C. Jolicoeur

Bibliographic record

VenueAmerican Antiquity · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsArcticArchaeologyTaphonomyGeographyThe arcticExcavationPrehistoryGeologyPhysical geographyOceanography

Abstract

fetched live from OpenAlex

In the first millennium AD, peoples across the North American Arctic began to use and exchange metal. A group known as the Late Dorset (AD 500–1300) were the first to widely exchange metal in the Eastern Arctic. However, due to differential taphonomic processes and past excavation methods, metal objects in existing collections are rare although geographically widespread. This has led to metal being seen as a broadly exchanged but uncommon raw material among Late Dorset. This article expands the known scale of Late Dorset metal use by analyzing the blade slot thicknesses of bone and ivory objects from sites across the Eastern Arctic and comparing them to the thicknesses of associated lithic and metal endblades. These results demonstrate that Late Dorset used metal at least as frequently as stone for some activities. Given the few and geographically discrete sources, metal would have been exchanged over thousands of kilometers of fragmented Arctic landscape. The lack of similar evidence in earlier periods indicates intergroup interaction increases significantly with the Late Dorset. It is through these same vectors that knowledge and information would have flowed. Metal, consequently, represents the best material for understanding the maximum extent and intensity of their interaction networks.

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 score0.061
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.225
Teacher spread0.195 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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