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Record W2947843622 · doi:10.1017/aaq.2018.92

New Insights from the Dorset Type Site at Alarniq, Northern Foxe Basin, Arctic Canada: Beach Level Chronology and Site Use

2019· article· en· W2947843622 on OpenAlexaffabout
Lesley Howse, James M. Savelle, Arthur S. Dyke

Bibliographic record

VenueAmerican Antiquity · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsDalhousie UniversityMcGill UniversityUniversity of Toronto
Fundersnot available
KeywordsRadiocarbon datingChronologyArchaeologyRidgeShoreArtifact (error)GeographyStructural basinGeologyArcticOceanographyPaleontology

Abstract

fetched live from OpenAlex

In 2008, four decades since Meldgaard's work at Alarniq—the type site for Dorset culture—Savelle and Dyke returned to resurvey the site. Archaeological investigations continued in 2015 and 2017 as part of the Foxe Basin Archaeological Project, when Howse conducted further surveys, excavated six semi-subterranean dwellings and two associated middens, and tested five additional features. The new site map and radiocarbon sequence have significantly changed our understanding of site use and beach-level chronology at Alarniq. The number of dwellings varies across the beach ridges, suggesting populations fluctuated throughout the site's use (2,700–800 cal BP). However, the new radiocarbon analyses also indicate that dwellings between 14.5 and 21.5 m above sea level are the same general age and that paleodemography at Alarniq is less straightforward than suggested by the number of features per beach ridge. It appears that ideal house construction location is a stronger indicator of the placement of winter houses at the site than proximity to the shoreline. We suggest this is largely related to site seasonality. These new data have significant implications for our understanding of current Dorset artifact typologies that have largely been developed using the material Meldgaard recovered at the site.

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 score0.086
Threshold uncertainty score0.928

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 studies0.0000.001
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 head0.013
GPT teacher head0.186
Teacher spread0.173 · 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
Published2019
Admission routes2
Has abstractyes

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