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Reliance Upon a Toxic Staple Crop

2017· book-chapter· en· W2626457251 on OpenAlexaff
Warren M. Wilson, Darna L. Dufour

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook-chapter
Languageen
FieldArts and Humanities
TopicAmazonian Archaeology and Ethnohistory
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStaple foodCropAmazon rainforestManihot esculentaIndigenousYield (engineering)AgroforestryAgricultureSelection (genetic algorithm)GeographyBiologyAgronomyEcologyComputer science

Abstract

fetched live from OpenAlex

Abstract In Amazonia most indigenous horticulturists prefer to cultivate the more toxic forms of manioc as a staple crop, despite the increased processing required to render them safe for consumption. This phenomenon has long intrigued anthropologists. In this chapter we describe the agricultural practices of the Tukanoan Indians in the North-west Amazon and explore their reliance on toxic varieties of manioc from agronomic, ecological, organoleptic, and ethnographic perspectives. Our findings indicate that the puzzling preference for a toxic staple crop may be explained by the higher yields produced by the more toxic forms, and also that the most salient factor in variety selection by Tukanoan women is the food into which the roots will be made. This suggests a multifaceted explanation. Moreover, we propose that present-day lack of concern about yield is a recent luxury due to artificial selection of sufficiently high-yielding manioc varieties during the development of this crop.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.209
Teacher spread0.164 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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