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Record W4323050517 · doi:10.1038/s43247-023-00726-4

Preclassic environmental degradation of Lake Petén Itzá, Guatemala, by the early Maya of Nixtun-Ch’ich’

2023· article· en· W4323050517 on OpenAlexaff
Brooke A. Birkett, Jonathan Obrist‐Farner, Prudence M. Rice, Wesley Parker, Peter Douglas, Melissa A. Berke, Audrey K. Taylor, Jason H. Curtis, Benjamin Keenan

Bibliographic record

VenueCommunications Earth & Environment · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsMcGill University
FundersMissouri University of Science and TechnologySociety for Sedimentary GeologyNational Science Foundation
KeywordsMayaEcosystemCharcoalArchaeologyGeographyPeriod (music)Human settlementYucatan peninsulaVegetation (pathology)EcologyBiology

Abstract

fetched live from OpenAlex

Abstract Paleolimnological evidence indicates the ancient Maya transformed terrestrial ecosystems by felling forest vegetation to construct large civic-ceremonial centers and to expand agriculture. Human settlements influenced lacustrine environments but the effects of Maya activities on aquatic ecosystems remain poorly studied. Here we analyzed a sediment core from Lake Petén Itzá, Guatemala, to infer paleoenvironmental changes resulting from Maya occupation of the archaeological site of Nixtun-Ch’ich’. Increases in charcoal and fecal stanol concentrations indicate Maya occupation of the Candelaria Peninsula by the late Early Preclassic period. Geochemical proxies reveal a period of lake ecosystem alteration during construction and expansion of the city’s urban grid in the Middle and Late Preclassic periods. Depopulation of the city in the Terminal Preclassic resulted in a decline in lake trophic state. Whereas previous studies of Petén waterbodies have indicated depressed lacustrine primary production, the core collected near Nixtun-Ch’ich’ shows evidence of ancient Maya lake ecosystem deterioration.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.208
Teacher spread0.185 · 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 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

Citations7
Published2023
Admission routes1
Has abstractyes

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