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Record W3134361473 · doi:10.3390/foods10030524

Historical Indigenous Food Preparation Using Produce of the Three Sisters Intercropping System

2021· review· en· W3134361473 on OpenAlexafffundabout
T.M. Ngapo, Pauline Bilodeau, Yves Arcand, Marie Thérèse Charles, Axel Diederichsen, Isabelle Germain, Qiang Liu, Shawna L. MacKinnon, Aimé J. Messiga, Martin Mondor, Sébastien Villeneuve, Noura Ziadi, Stéphane Gariépy

Bibliographic record

VenueFoods · 2021
Typereview
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsIndigenousIntercroppingFood systemsCroppingFood preparationAgroforestryTraditional knowledgeGeographyFood securityEthnologyBiotechnologyAgricultureFood processingAgronomyHistoryFood scienceBiologyArchaeologyEcology

Abstract

fetched live from OpenAlex

For centuries, some Indigenous Peoples of the Americas have planted corn, beans and squash or pumpkins together in mounds, in an intercropping complex known as the Three Sisters. Agriculturally, nutritionally and culturally, these three crops are complementary. This literature review aims to compile historical foods prepared from the products of the Three Sisters planting system used in Indigenous communities in the region encompassing southern Quebec and Ontario in Canada, and northeastern USA. The review does not discuss cultural aspects of the Three Sisters cropping system or describe foods specific to any one Indigenous group, but rather, gives an overview of the historical foods stemming from this intercropping system, many foods of which are common or similar from one group to another. Some of the methods of food preparation used have continued over generations, some of the historical foods prepared are the foundation for foods we eat today, and some of both the methods and foods are finding revival.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.216
GPT teacher head0.443
Teacher spread0.227 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations35
Published2021
Admission routes3
Has abstractyes

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