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Record W1497180129 · doi:10.1111/nyas.12737

Carbon stable‐isotope tracking in breath for comparative studies of fuel use

2015· review· en· W1497180129 on OpenAlexafffund
Kenneth C. Welch, François Péronnet, Kent A. Hatch, Christian C. Voigt, Marshall D. McCue

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2015
Typereview
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversité de MontréalThe Scarborough HospitalUniversity of Toronto
FundersCompany of BiologistsNatural Sciences and Engineering Research Council of CanadaSt Mary's University
KeywordsRespirometryStable isotope ratioBiochemical engineeringIsotopeTRACEREcologyEnvironmental chemistryBiologyEnvironmental scienceChemistryBiochemistryEngineering

Abstract

fetched live from OpenAlex

Almost half a century ago, researchers demonstrated that the ratio of stable carbon isotopes in exhaled breath of rats and humans could reveal the oxidation of labeled substrates in vivo, opening a new chapter in the study of fuel use, the fate of ingested substrates, and aerobic metabolism. Until recently, the combined use of respirometry and stable-isotope tracer techniques had not been broadly employed to study fuel use in other animal groups. In this review, we summarize the history of this approach in human and animal research and define best practices that maximize its utility. We also summarize several case studies that use stable-isotope measurements of breath to explore the limits of aerobic metabolism and substrate turnover among several species and various physiological states. We highlight the importance of a comparative approach in revealing the profound effects that phylogeny, ecology, and behavior can have in shaping aerobic metabolism and energetics as well as the fundamental biological principles that underlie fuel use and metabolic function across taxa. New analytical equipment and refinement of methodology make the combined use of respirometry and stable-isotope tracer techniques simpler to perform, less costly, and more field ready than ever before.

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)0.003
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 score0.893
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.402
GPT teacher head0.457
Teacher spread0.056 · 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

Citations55
Published2015
Admission routes2
Has abstractyes

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