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A survey of the traditional food consumption that may contribute to enhanced soil ingestion in a Canadian First Nation community

2012· article· en· W2045565133 on OpenAlexaffabout
James R. Doyle, Jules M. Blais, Paul A. White

Bibliographic record

VenueThe Science of The Total Environment · 2012
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsHealth CanadaUniversity of Ottawa
Fundersnot available
KeywordsIngestionConsumption (sociology)Environmental scienceFood consumptionEnvironmental healthGeographyEnvironmental protectionAgricultural economicsBiologyMedicineSociologyEconomicsSocial science

Abstract

fetched live from OpenAlex

Soil ingestion rates in the order of 400 mg d(-1) have been proposed and considered plausible for use in human health risk assessments (HHRA) of Aboriginal populations and justified by qualitative assessments of the traditional subsistence activities that could enhance soil ingestion. The purpose of this study was to assess and document the subsistence activities and food consumption practiced by a First Nation Community living in a wilderness community in Canada to allow for a comparison with the previous qualitative assessments of Aboriginal populations and a quantitative mass balance tracer element study of the community conducted concurrently. An ethno-cultural survey was conducted of the Xeni Gwet'in First Nations community living in the Nemiah Valley, approximately 230 km west of Williams Lake, British Columbia. The community diet was observed to consist mainly of fish and big game, and was supplemented by berries and roots. Outdoor cultural gatherings, hunting and food gathering trips and sporting events, with their attendant potential for enhanced soil exposure, were observed to be an important facet of community life. The survey concluded that a significant portion of the Xeni Gwet'in practise a lifestyle similar to the subsistence lifestyles of other indigenous communities, where soil exposure scenarios in the order of hundreds of mg d(-1) have been proposed.

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)0.006
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 score0.229
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.181
GPT teacher head0.347
Teacher spread0.166 · 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

Citations6
Published2012
Admission routes2
Has abstractyes

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