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Record W4402903903 · doi:10.1126/sciadv.adq4127

Greenland Norse walrus exploitation deep into the Arctic

2024· article· en· W4402903903 on OpenAlexaffabout
Emily J. Ruiz‐Puerta, Greer Jarrett, Morgan L. McCarthy, Shyong En Pan, Xénia Keighley, Magie Aiken, Giulia Zampirolo, Maarten J. J. E. Loonen, Anne Birgitte Gotfredsen, Lesley Howse, Paul Szpak, Snæbjörn Pálsson, Scott Rufolo, Hilmar J. Malmquist, Sean P. A. Desjardins, Morten Tange Olsen, Peter Jordan

Bibliographic record

VenueScience Advances · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsTrent UniversityNunavut Research InstituteCanadian Museum of Nature
FundersHorizon 2020Háskóli ÍslandsArts and Humanities Research CouncilLunds UniversitetNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsArcticIndigenousGeographyThe arcticArchaeologyRange (aeronautics)Circumpolar starHuman settlementFisheryOceanographyEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Walrus ivory was a prized commodity in medieval Europe and was supplied by Norse intermediaries who expanded across the North Atlantic, establishing settlements in Iceland and Greenland. However, the precise sources of the traded ivory have long remained unclear, raising important questions about the sustainability of commercial walrus harvesting, the extent to which Greenland Norse were able to continue mounting their own long-range hunting expeditions, and the degree to which they relied on trading ivory with the various Arctic Indigenous peoples that they were starting to encounter. We use high-resolution genomic sourcing methods to track walrus artifacts back to specific hunting grounds, demonstrating that Greenland Norse obtained ivory from High Arctic waters, especially the North Water Polynya, and possibly from the interior Canadian Arctic. These results substantially expand the assumed range of Greenland Norse ivory harvesting activities and support intriguing archaeological evidence for substantive interactions with Thule Inuit, plus possible encounters with Tuniit (Late Dorset Pre-Inuit).

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.517

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

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