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Record W4403225308 · doi:10.1016/j.jasrep.2024.104791

Ancient mitochondrial DNA extraction from Bison bison long bones from Head-Smashed-In Buffalo Jump, UNESCO World Heritage site

2024· article· en· W4403225308 on OpenAlexafffundabout
Rexelle Asis, Skyler Ngo, Mavis Chan, Shawn Bubel, Theresa M. Burg

Bibliographic record

VenueJournal of Archaeological Science Reports · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsUniversity of Lethbridge
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of Lethbridge
KeywordsAncient DNAMitochondrial DNAArchaeologyHead (geology)GeographyWorld heritageJumpPaleontologyBiologyGeneticsDemography

Abstract

fetched live from OpenAlex

Head-Smashed-In Buffalo Jump (HSIBJ) is a UNESCO World Heritage Site located on the southern end of the Porcupine Hills, near Fort Macleod, Alberta, Canada. Using a system of drive lanes, Indigenous groups drove herds of bison over the cliff edge for thousands of years. The well-stratified deposits at the base of the cliff offer a unique opportunity to investigate the genetic diversity of the American bison before European contact and their genetic bottleneck in the 19th century. We extracted ancient DNA from twenty-one bison long bones, amplified and sequenced the mitochondrial DNA control region. Comparisons between ancient and modern bison populations revealed novel haplotypes in the HSIBJ population, suggesting a loss of genetic diversity due to the bottleneck. Furthermore, we discovered a shared haplotype between the bison hunted at the site and modern populations, which may help elucidate the complex history of living herds.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.033
GPT teacher head0.304
Teacher spread0.272 · 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 designBench or experimental
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

Citations0
Published2024
Admission routes3
Has abstractyes

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