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Record W4387533006 · doi:10.15184/aqy.2023.131

Mixed ancestry of Europeans who settled Iceland and Greenland: 3D geometric-morphometric analyses of cranial base shape

2023· article· en· W4387533006 on OpenAlexafffund
Kimberly A. Plomp, Keith Dobney, Hildur Gestsdóttir, Mark Collard

Bibliographic record

VenueAntiquity · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsSimon Fraser University
FundersH2020 Marie Skłodowska-Curie ActionsEuropean CommissionBritish Columbia Knowledge Development FundCanada Research ChairsSimon Fraser UniversitySocial Sciences and Humanities Research Council of Canada
KeywordsCraniaIrishGeographyArchaeologyViking AgePeriod (music)GenealogyHistoryDemographyArt

Abstract

fetched live from OpenAlex

Debate surrounds the identity of the Europeans who settled Iceland and Greenland in the early medieval period. Historical sources record settlers travelling from Norway to Iceland and then Greenland, but recent analyses of biological data suggest that some settlers had British and Irish ancestry. Here, the authors test these hypotheses with 3D-shape analyses of human crania from Scandinavia, Britain and Ireland, and one of the Norse colonies in Greenland. Results suggest that some 63 per cent of the ancestry of the Greenlandic individuals can be traced to Britain and Ireland and 37 per cent to Scandinavia. These findings add further weight to the idea that the European settlers who colonised Iceland and later Greenland were of mixed ancestry.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.142
GPT teacher head0.328
Teacher spread0.186 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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