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Record W2738723251 · doi:10.1017/laq.2017.13

EXAMINING CHRONOLOGICAL TRENDS IN ANCIENT MAYA DIET AT MINANHA, BELIZE, USING THE STABLE ISOTOPES OF CARBON AND NITROGEN

2017· article· en· W2738723251 on OpenAlexafffund
Jocelyn S. Williams, Shannen M. Stronge, Gyles Iannone, Fred J. Longstaffe

Bibliographic record

VenueLatin American Antiquity · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsWestern UniversityGolder Associates (Canada)Trent University
FundersTrent University
KeywordsSubsistence agricultureStable isotope ratioMayaIsotopes of nitrogenIsotope analysisδ13CIsotopes of carbonElitePsychological resilienceBiomass (ecology)GeographyEcologyBiologyArchaeologyPoliticsTotal organic carbonAgriculture

Abstract

fetched live from OpenAlex

We present the results of stable carbon and nitrogen isotope analyses of bone collagen and bone bioapatite from the ancient Maya center of Minanha, Belize (ca. 100 B.C. to A.D. 1260). The purpose of this research was to reconstruct diet and investigate the influence of sociopolitical and environmental factors. Overall, diet was relatively stable over time, with maize being a staple in all periods. Maize consumption reached its peak in the transitional Early to Middle Classic periods and decreased over time. When isotope data from dry periods were compared to normal periods, there were no significant differences, although comparisons of isotope data by burial location and type suggest that the apical or ruling elite consumed a more diverse diet, with more animal protein, relative to the lesser elites. The temporal variability in maize consumption seems best explained by sociopolitical factors documented at Minanha and within the Vaca Plateau. This study demonstrates the resilience of ancient subsistence practices in the face of climatic instability and highlights the impact that social and political factors can have on diet and subsistence economy.

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 score0.021
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.041
GPT teacher head0.260
Teacher spread0.219 · 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

Citations11
Published2017
Admission routes2
Has abstractyes

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