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Record W2116281775 · doi:10.1002/gea.20352

Environmental and cultural changes in highland Guatemala inferred from Lake Amatitlán sediments

2011· article· en· W2116281775 on OpenAlexaff
María I. Vélez, Jason H. Curtis, Mark Brenner, Jaime Escobar, Barbara W. Leyden, Marion Popenoe de Hatch

Bibliographic record

VenueGeoarchaeology · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsDeforestation (computer science)MayaReforestationHoloceneGeographyEutrophicationPopulationEnvironmental changePaleolimnologyEcologySedimentClimate changeArchaeologyGeologyNutrientForestryPaleontologyBiology

Abstract

fetched live from OpenAlex

Abstract We inferred the late Holocene environmental history of the Guatemala highlands from multiple lines of evidence in a sediment core from Lake Amatitlán. Inferred environmental changes are generally synchronous with archaeologically documented highland Maya cultural shifts. Population increases in the Middle Preclassic, Early Classic, and Late Postclassic are associated with deforestation and soil erosion. Land abandonment in the Late Preclassic, Late Classic, and Early Postclassic is associated with evidence for reforestation and soil stabilization. Diatoms indicate relatively lower lake level and greater trophic status at times of reduced human impact, from ca. 250 B.C. to A.D. 125 and from ca. A.D. 875 to 1375. Decreased water levels were probably due to drier climate, to reforestation, or both. Lake eutrophication was caused by reduced water volume combined with a legacy of long‐term agricultural activity. Our data contribute to the understanding of relations among ancient Maya culture, climate, and environment. © 2011 Wiley Periodicals, Inc.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.018
GPT teacher head0.188
Teacher spread0.171 · 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

Citations31
Published2011
Admission routes1
Has abstractyes

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