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Zooarchaeology of the Scandinavian settlements in Iceland and Greenland

2017· book· en· W2747563394 on OpenAlexaff
Konrad Śmiarowski, Ramona Harrison, Seth Brewington, Megan Hicks, Frank Feeley, Céline Dupont-Hébert, Brenda Prehal, George Hambrecht, James Woollett, Thomas H. McGovern

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHuman settlementZooarchaeologyGeographyArchaeologySocial organizationHistoryEthnologyEcologyAnthropologySociology

Abstract

fetched live from OpenAlex

The Scandinavian Viking Age and Medieval settlements of Iceland and Greenland have been subject to zooarchaeological research for over a century, and have come to represent two classic cases of survival and collapse in the literature of long-term human ecodynamics. The work of the past two decades by multiple projects coordinated through the North Atlantic Biocultural Organization (NABO) cooperative and by collaborating scholars has dramatically increased the available zooarchaeological evidence for economic organization of these two communities, their initial adaptation to different natural and social contexts, and their reaction to Late Medieval economic and climate change. This summary paper provides an overview of ongoing comparative research as well as references for data sets and more detailed discussion of archaeofauna from these two island communities.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.215
Teacher spread0.194 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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