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Record W4365136047 · doi:10.1002/bies.202300026

Solving the conundrum of intra‐specific variation in metabolic rate: A multidisciplinary conceptual and methodological toolkit

2023· article· en· W4365136047 on OpenAlex
Neil B. Metcalfe, Jakob Bellman, Pierre Bize, Pierre Blier, Amélie Crespel, Neal J. Dawson, Ruth E. Dunn, Lewis G. Halsey, Wendy R. Hood, Mark Hopkins, Shaun S. Killen, Darryl McLennan, Lauren E. Nadler, Julie J. H. Nati, Matthew J. Noakes, Tommy Norin, Susan E. Ozanne, M. Peaker, Amanda K. Pettersen, Anna S. Przybylska-Piech, A. Rathery, Charlotte Récapet, Enrique Rodríguez, Karine Salin, Antoine Stier, Elisa Thoral, Klaas R. Westerterp, Margriet S. Westerterp‐Plantenga, Michał S. Wojciechowski, Pat Monaghan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioEssays · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsMemorial University of NewfoundlandUniversité du Québec à Rimouski
FundersMedical Research CouncilBritish Heart FoundationVillum Fonden
KeywordsTerminologyBiomedicineVariation (astronomy)Multidisciplinary approachContext (archaeology)Data scienceConceptual frameworkBiologyManagement scienceEcologyEngineering ethicsComputer scienceSociologyBioinformaticsSocial science

Abstract

fetched live from OpenAlex

Researchers from diverse disciplines, including organismal and cellular physiology, sports science, human nutrition, evolution and ecology, have sought to understand the causes and consequences of the surprising variation in metabolic rate found among and within individual animals of the same species. Research in this area has been hampered by differences in approach, terminology and methodology, and the context in which measurements are made. Recent advances provide important opportunities to identify and address the key questions in the field. By bringing together researchers from different areas of biology and biomedicine, we describe and evaluate these developments and the insights they could yield, highlighting the need for more standardisation across disciplines. We conclude with a list of important questions that can now be addressed by developing a common conceptual and methodological toolkit for studies on metabolic variation in animals.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.086
GPT teacher head0.298
Teacher spread0.212 · 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