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Record W4312218316 · doi:10.1177/01976931221146570

Interpreting δ <sup>13</sup> C Values Obtained on SOM from Ancient Maya Reservoirs and Depressions

2022· article· en· W4312218316 on OpenAlexafffund
Kenneth B. Tankersley, Nicholas P. Dunning, David L. Lentz, Christopher Carr, Liwi Grazioso, Trinity L. Hamilton, Kathryn Reese‐Taylor

Bibliographic record

VenueNorth American Archaeologist · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaAlphawood FoundationNational Science Foundation
KeywordsOrganic matterCyperaceaeMayaCyanobacteriaAlgaeSoil organic matterEnvironmental sciencePoaceaeEcologyGeologyArchaeologyEnvironmental chemistrySoil waterGeographyChemistryBiologySoil sciencePaleontology

Abstract

fetched live from OpenAlex

Elemental analyzer (EA) Isotope Ratio Mass Spectrometry was used to measure ∂ 13 C values on soil organic matter from reservoirs and depressions at the ancient Maya urban centers of Tikal, Guatemala and Yaxnohcah, Mexico. Variation in δ 13 C values on soil organic matter were > −2.0‰, which suggests enrichment from C4 plants including maize, other tropical grasses (Poaceae), and tropical sedges (Cyperaceae), CAM plants (Clusia sp.), and cyanobacteria (blue-green algae). Cyanobacteria were likely a major contributor to the 13C enrichment of soil organic matter in Maya reservoirs and depressions, which has obfuscated our understanding of ancient Maya maize production. It is possible that the Maya used cyanobacteria as a fertilizer, which enriched agricultural field soil organic matter.

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)0.000
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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.009
GPT teacher head0.239
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

Citations1
Published2022
Admission routes2
Has abstractyes

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