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Record W4407227217 · doi:10.1017/aaq.2024.51

Farmers with a Taste for Fish: New Insights into Iroquoian Foodways at the Dawson Site

2025· article· en· W4407227217 on OpenAlexaffabout
Karine Taché, Roland Tremblay, Alexandre Lucquin, Marjolein Admiraal, John P. Hart, Oliver E. Craig

Bibliographic record

VenueAmerican Antiquity · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversité Laval
FundersQueens College, City University of New YorkCity University of New York
KeywordsFoodwaysPotteryArchaeologyCrayfishRange (aeronautics)Fish <Actinopterygii>GeographyFisheryBiologyArt

Abstract

fetched live from OpenAlex

Abstract Iroquoian groups inhabiting the St. Lawrence Valley in the fifteenth and sixteenth centuries AD practiced agriculture and supplemented their diet with fish and a variety of wild plants and terrestrial animals. Important gaps remain in our knowledge of Iroquoian foodways, including how pottery was integrated to culinary practices and the relative importance of maize in clay-pot cooking. Lipid analyses carried out on 32 potsherds from the Dawson site (Montreal, Canada) demonstrate that pottery from this village site was used to prepare a range of foodstuffs—primarily freshwater fish and maize, but possibly also other animals and plants. The importance of aquatic resources is demonstrated by the presence of a range of molecular compounds identified as biomarkers for aquatic products, whereas the presence of maize could only be detected through isotopic analysis. Bayesian modeling suggests that maize is present in all samples and is the dominant product in at least 40% of the potsherds analyzed. This combination of analytical techniques, applied for the first time to Iroquoian pottery, provides a glimpse into Iroquoian foodways and suggests that sagamité was part of the culinary traditions at the Dawson site.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.006
GPT teacher head0.244
Teacher spread0.238 · 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 designNot applicable
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

Citations2
Published2025
Admission routes2
Has abstractyes

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