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Record W2048499822 · doi:10.2307/25470442

Radiocarbon Dating and Dietary Stable Isotope Analysis of Kwaday Dän Ts'inchí

2007· article· en· W2048499822 on OpenAlexaffabout
Michael P. Richards, Sheila Greer, Lorna T. Corr, Owen Beattie, Alexander Mackie, Richard P. Evershed, Al von Finster, John Southon

Bibliographic record

VenueAmerican Antiquity · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsFisheries and Oceans CanadaRoyal British Columbia MuseumUniversity of AlbertaFirst Nations Health and Social Secretariat of Manitoba
Fundersnot available
KeywordsRadiocarbon datingIsotope analysisGlacierGeologyHuman boneArchaeologyStable isotope ratioPeriod (music)IsotopePhysical geographyGeographyPaleontologyOceanographyChemistryPhysics

Abstract

fetched live from OpenAlex

We report here on the results of AMS dating and isotopic analysis of the frozen human remains named Kwaday Dän Ts'inchí and associated materials recovered from a glacier located in Northwest British Columbia, Canada in 1999. The isotopic analysis of bone collagen (bulk and single amino acids) from the individual indicates a strongly marine diet, which was unexpected given the location of this find, more than 100 km inland eroding out of a high elevation glacier; however, bulk hair and bone cholesterol isotopic values indicate a shift in diet to include more terrestrial foods in the year before death. The radiocarbon dating is not straightforward, as there are difficulties in determining the appropriate marine correction for the human remains, and the spread of dates on the associated artifacts clearly indicates that this was not a single use site. By combining the most recent date on a robe worn by Kwaday Dän Ts'inchi with direct bone collagen dates we conclude that the individual likely dates to between cal A.D. 1670 to 1850, which is in the pre-(or early) European contact period for this region.

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 score0.063
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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

Citations29
Published2007
Admission routes2
Has abstractyes

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