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Record W2994602971 · doi:10.1080/00934690.2019.1684748

Centering the Classic Maya Kingdom of Sak Tz’i’

2019· article· en· W2994602971 on OpenAlexaff
Charles J. Golden, A. Scherer, Stephen Houston, Whittaker Schroder, Shanti Morell‐Hart, Socorro del Pilar Jiménez Álvarez, George Van Kollias, Moisés Yerath Ramiro Talavera, Mallory E. Matsumoto, Jeffrey Dobereiner, Omar Alcover Firpi

Bibliographic record

VenueJournal of Field Archaeology · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMayaArchaeologyKingdomHistoryAncient historyArtGeographyGeologyPaleontology

Abstract

fetched live from OpenAlex

In this article, we provide the results of preliminary archaeological and epigraphic research undertaken at the site of Lacanjá Tzeltal, Chiapas. Field research conducted in 2018, in collaboration with local community members, has allowed us to identify this archaeological site as the capital of a kingdom known from Classic period Maya inscriptions as “Sak Tz’i’” (White Dog). Because all previously known references to the kingdom came from looted monuments or texts found at other Maya centers, the location of the Sak Tz’i’ kingdom’s capital has been the subject of ongoing modeling and debate among scholars. Here we synthesize prior epigraphic and archaeological research concerning Sak Tz’i’, highlighting past efforts to locate the kingdom’s capital. We then discuss the results of preliminary survey, mapping, and excavations of Lacanjá Tzeltal, and present the first drawing and decipherment of Lacanjá Tzeltal Panel 1, the sculpture crucial for centering this “lost” Maya kingdom.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.252
Teacher spread0.230 · 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 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

Citations53
Published2019
Admission routes1
Has abstractyes

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