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Record W2797088421 · doi:10.1017/aaq.2018.10

EARLIEST MICROBOTANICAL EVIDENCE FOR MAIZE IN THE NORTHERN LAKE MICHIGAN BASIN

2018· article· en· W2797088421 on OpenAlexaboutno aff
Rebecca K. Albert, Susan M. Kooiman, Caitlin A. Clark, William A. Lovis

Bibliographic record

VenueAmerican Antiquity · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
Fundersnot available
KeywordsPeninsulaGeographyStructural basinBiological dispersalArchaeologyZea maysPhysical geographyAccelerator mass spectrometryGeologyRadiocarbon datingBiologyAgronomyPaleontology

Abstract

fetched live from OpenAlex

There is no recorded maize ( Zea mays spp. mays ) from sites predating circa cal AD 800 in the northern Lake Michigan or Lake Superior basins of the western Great Lakes, despite the presence of maize microbotanicals including phytoliths and starches in Michigan, New York, and Quebec as early as 400 cal BC. To evaluate the potential for an earlier maize presence in the northern Lake Michigan basin, samples of carbonized food residues adhering to 16 ceramic vessels were obtained from the Winter site (20DE17) located on the Garden Peninsula in the northern Lake Michigan basin. Each sample was split and sent to two analysts. Both analysts identified low incidences of maize starch and phytoliths in multiple samples, with overlapping identifications on several. Three direct accelerator mass spectrometry dates on the carbonized residues reveal maize incorporated into the residues as early as the second century cal BC, 800 years before any regional macrobotanical evidence. Although the method of dispersal cannot be determined, these results support the proposition that initial northern dispersal of maize in the region may have been nearly 800 years earlier than macrobotanical evidence would suggest and is consistent with the timing of its introduction to the lower Great Lakes area.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.802

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.280
Teacher spread0.245 · 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

Citations22
Published2018
Admission routes1
Has abstractyes

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