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Record W4396695072 · doi:10.1080/0734578x.2024.2318835

Characterizing variation in late precontact diets using dental microwear texture analysis: a Caborn-Welborn example

2024· article· en· W4396695072 on OpenAlexaff
G. Holmes, Christopher W. Schmidt, Christopher Moore

Bibliographic record

VenueSoutheastern Archaeology · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsVariation (astronomy)ArchaeologyTexture (cosmology)GeographyHistoryComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The Caborn-Welborn phase identifies late precontact and early postcontact (ca. AD 1400–1700) peoples of the lower Ohio River valley in Indiana, Kentucky, and Illinois who coalesced following the collapse of the Angel chiefdom. Our current understanding of Caborn-Welborn foodways is that they, like their contemporaries in other parts of eastern North America, subsisted largely on maize and wild game, supplemented by a wide range of other wild and cultivated foods. While technically accurate, this generic picture of late precontact diets does not do justice to the many microclimatological and cultural nuances that characterized local native southeastern and midwestern diets. Dental microwear texture analysis (DMTA) is a method to understand diet by analyzing the microscopic scratches and pits left on teeth during mastication. Combining DMTA with other lines of evidence, we confirm previous studies that indicate that at least some Caborn-Welborn peoples consumed less maize than their ancestors living at the Angel site. Rather than understanding Caborn-Welborn peoples as practicing a generic late precontact subsistence strategy, our study highlights how Caborn-Welborn peoples chose to eat less maize and consume more hard wild foods such as nuts compared with their contemporaries.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
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
Bibliometrics0.0010.000
Science and technology studies0.0000.005
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 head0.031
GPT teacher head0.262
Teacher spread0.231 · 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.

Study designQualitative
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

Citations1
Published2024
Admission routes1
Has abstractyes

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