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Record W3083256194 · doi:10.24908/iqurcp.10656

11. What’s in a Loaf? A Bio-accessibility Study of Toxic Elements in Gluten-free and Rye Breads and the Effect of Toasting Bread on the Bio-accessibility of those Elements Using ICP-MS

2018· article· en· W3083256194 on OpenAlexvenueno aff
Alastair Kierulf

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldChemistry
TopicHeavy Metals in Plants
Canadian institutionsnot available
Fundersnot available
KeywordsCadmiumGluten freeFood scienceArsenicGlutenLeaching (pedology)SeleniumChemistryBiologySoil waterEcology

Abstract

fetched live from OpenAlex

Bread is a staple in the North American diet with over 4 million tonnes consumed in the US annually. The popularity of breads made from alternative grains (such as rye, quinoa, and pumpernickel) and the increase in gluten free (GF) alternatives (made from a mixture of rice and other alternative grains) has significantly contributed to this growth[1] While the hunger for alternative breads is increasing, there is little research into the risks associated with consuming breads made from alternative grains. Studies[2] have shown that many grains can contain high levels of toxic elements, especially if they are grown in soils with high levels of these elements.
 A previous study [3] investigated the risk associated with toxic elements in rice, and concluded that these elements are highly bio-accessible when the rice is not washed before processing. It is therefore extremely important to investigate the risk and bio-accessibility of toxic elements in these popular alternative breads.
 Two different alternative breads were analyzed for their toxic element compositions, and the bio-accessibility of these elements was investigated. Results showed that the gluten free breads contained high concentrations of arsenic and selenium, and little to no levels of cadmium or lead. Rye bread, in comparison, contained little arsenic, cadmium, or selenium which was consistent with previous studies. Further work will investigate the effect of toasting and will also utilize an innovative online leaching method that significantly improves bio-accessibility results [3,4].

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)0.016
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0000.001
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 head0.127
GPT teacher head0.413
Teacher spread0.286 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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