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Record W4312131260 · doi:10.1007/s10814-022-09180-w

Agriculture in the Ancient Maya Lowlands (Part 1): Paleoethnobotanical Residues and New Perspectives on Plant Management

2022· article· en· W4312131260 on OpenAlexafffund
Shanti Morell‐Hart, Lydie Dussol, Scott L. Fedick

Bibliographic record

VenueJournal of Archaeological Research · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsMcMaster University
FundersCentre National de la Recherche ScientifiqueMcMaster UniversityNational Science Foundation
KeywordsAgricultureMayaAgroforestrySlash-and-burnIndigenousGeographyPaleoethnobotanyDominance (genetics)Traditional knowledgePrehistoryFood securityArchaeologyEcologyBiology

Abstract

fetched live from OpenAlex

Abstract We focus on pre-Columbian agricultural regimes in the Maya Lowlands, using new datasets of archaeological wood charcoal, seeds, phytoliths, and starch grains; biological properties of plants; and contemporary Indigenous practices. We address inherited models of agriculture in the lowlands: the limitations of the environment (finding more affordances than anticipated by earlier models); the homogeneity of agricultural strategies (finding more heterogeneity of strategies across the lowlands than a single rigid template); the centrality of maize in agriculture (finding more reliance on root crops and tree crops than historically documented); the focus on the milpa system as food base (finding more agroforestry, homegardening, horticulture, and wild resource management than previously documented); the dominance of swidden strategies in agricultural practices (finding more diverse practices than accounted for in most models); and the foregrounding of maize crop failure in collapse models (finding more evidence of resilience and sustainable agricultural practices than predicted).

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)0.003
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 score0.034
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.077
GPT teacher head0.296
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

Citations30
Published2022
Admission routes2
Has abstractyes

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