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Record W3096725799 · doi:10.1093/nutrit/nuaa109

Natural abundance carbon isotope ratio analysis and its application in the study of diet and metabolism

2020· review· en· W3096725799 on OpenAlexafffund
R. J. Scott Lacombe, Richard P. Bazinet

Bibliographic record

VenueNutrition Reviews · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsIsotopes of carbonIsotope analysisEnvironmental chemistryAbundance (ecology)Carbon fibersIsotopeIsotope-ratio mass spectrometryNatural abundanceStable isotope ratioChemistryEnvironmental scienceBiologyEcologyTotal organic carbonMass spectrometryMaterials science

Abstract

fetched live from OpenAlex

Due to differences in carbon assimilation pathways between plants, there are subtle but distinct variations in the carbon isotope ratios of foods and animal products throughout the food supply. Although it is well understood that the carbon isotope ratio composition of the diet influences that of the consumers' tissues, the application of natural abundance carbon isotope ratio analysis in nutrition has long been underappreciated. Over the past decade, however, several studies have investigated the utility of carbon isotope ratio analysis for evaluation of nutritional biomarker status, primarily focusing on its application as an objective indicator of sugar and animal protein intake. More recently, research investigating the application of natural abundance measurements has been extended to study fatty acid metabolism and has yielded encouraging results. Collectively, data from large-scale observational studies and experimental animal studies highlight the potential for carbon isotope ratio analysis as an additional and effective tool to study diet and metabolism. The purpose of this review is to provide an overview of natural abundance carbon isotope ratio analysis, its application to studying nutrition, and an update of the research in the field.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
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 head0.024
GPT teacher head0.309
Teacher spread0.284 · 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
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

Citations21
Published2020
Admission routes2
Has abstractyes

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