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Record W3036520676 · doi:10.1016/j.jasrep.2020.102424

Diversity in Labrador Inuit sled dog diets: Insights from δ13C and δ15N analysis of dog bone and dentine collagen

2020· article· en· W3036520676 on OpenAlexafffundabout
Alison Harris, Deirdre A. Elliott, Eric Guiry, Matthew von Tersch, Lisa Rankin, Peter Whitridge, Michelle Alexander, Gunilla Eriksson, Vaughan Grimes

Bibliographic record

VenueJournal of Archaeological Science Reports · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsTrent UniversityUniversity of British ColumbiaMemorial University of Newfoundland
FundersHorizon 2020Social Sciences and Humanities Research Council of CanadaHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsFaunaMammalRaccoon Dogsδ13CGeographySubsistence agricultureMustelidaeAnimal scienceBiologyArchaeologyZoologyEcologyStable isotope ratio

Abstract

fetched live from OpenAlex

Sled dogs were an integral part of Labrador Inuit life from the initial expansion and settlement of northeastern Canada to the present day. Tasked with pulling sleds and assisting people with other subsistence activities in the winter, dogs required regular provisioning with protein and fat. In this paper, we conduct stable carbon and nitrogen isotope ratio analysis of the skeletal remains of dogs (n = 35) and wild fauna (n = 68) from sites located on the north and south coasts of Labrador to characterize dog provisioning between the 15th to early 19th centuries. In addition, we analyse bone (n = 20) and dentine (n = 4) collagen from dogs from Double Mer Point, a communal house site in Hamilton Inlet to investigate how dog diets intersected with Inuit subsistence and trade activities at a local level. We find that dog diets were largely composed of marine mammal protein, but that dogs on the north coast consumed more caribou and fish relative to dogs from the central and south coast sites. The diets of dogs from Double Mer Point were the most heterogenous of any site, suggesting long-distance movement of people and/or animals along the coast.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.017
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.003
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 head0.015
GPT teacher head0.243
Teacher spread0.229 · 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
Published2020
Admission routes3
Has abstractyes

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