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Record W3193721840 · doi:10.1017/aaq.2021.58

Testing for Mississippian Period Turkey Management in the Archaeological Record of the Southeastern United States

2021· article· en· W3193721840 on OpenAlexaffabout
Erin Kennedy Thornton, Tanya M. Peres, Kelly Ledford Chase, Brian M. Kemp, Ryan Frome, Aurélie Manin, Lauren Basnett, Krista McGrath, Camilla Speller, Elizabeth J. Reitz

Bibliographic record

VenueAmerican Antiquity · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMesoamericaDomesticationMeleagris gallopavoAnimal husbandryArchaeologyGeographyPeriod (music)LivestockFeatherSocioeconomicsEthnologyAgricultureEcologyHistoryZoologyBiologyForestry

Abstract

fetched live from OpenAlex

People living in Mesoamerica and what is now the eastern and southwestern United States used turkeys ( Meleagris gallopavo ) as sources of meat, eggs, bones, and feathers. Turkey husbandry and domestication are confirmed in two of these regions (Mesoamerica and the American Southwest), but human-turkey interactions in Eastern North American (eastern United States and Canada) are not fully explored. We apply stable isotope (δ 13 C, δ 15 N) and ancient mitochondrial DNA analyses to archaeofaunal samples from seven sites in the southeastern United States to test whether turkeys were managed or captively reared. These combined data do not support prolonged or intensive captive rearing of turkeys, and evidence for less intensive management is ambiguous. More research is warranted to determine whether people managed turkeys in these areas, and whether this is generalizable. Determining whether turkeys were managed or reared in the southeastern United States helps define cultural and environmental factors related to turkey management or husbandry throughout North America. This inquiry contributes to discussion of the roles of intensified human-animal interactions in animal domestication.

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 score0.035
Threshold uncertainty score0.321

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 head0.022
GPT teacher head0.271
Teacher spread0.249 · 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

Citations8
Published2021
Admission routes2
Has abstractyes

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