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Scaling Temperature Effects on Metabolism from Individuals to Ecosystems

2025· article· en· W4414041805 on OpenAlexaff
Mary I. O’Connor, David Anderson, Nicole S. Knight, Margaret A. Slein, Keila Stark

Bibliographic record

VenueAnnual Review of Ecology Evolution and Systematics · 2025
Typearticle
Languageen
FieldChemistry
Topicthermodynamics and calorimetric analyses
Canadian institutionsUniversity of GuelphUniversity of British Columbia
Fundersnot available
KeywordsScalingAllometryEcosystemMetabolic rateGlobal changePopulationEcological systems theoryEnvironmental changeAquatic ecosystem

Abstract

fetched live from OpenAlex

The effects of temperature on metabolic rates are a core component of ecological change, with surprisingly regular effects across diverse ecological systems. Metabolic scaling theories can provide quantitative explanations for change in population, community, and ecosystem processes by relating ecological processes to the temperature dependence of metabolic processes, specifically major and highly conserved processes such as photosynthesis and respiration. Differences in scaling models for temperature relative to allometric scaling models affect the application of scaling theory to ecological systems. Recent theoretical and empirical advances include extending the theory beyond equilibrium conditions to test the effects of scaling on changes at population and community levels of organization, with particular success in aquatic systems. Challenges remain for advancing scaling theory for dynamic systems, mutualisms, and the effects of thermal asymmetries. Still, the scaling literature offers a rich body of knowledge that touches many ecological disciplines and is rapidly advancing to meet the challenges of integrating knowledge of ecological change in an operational framework.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.267
Teacher spread0.261 · 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 designSystematic review
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

Citations5
Published2025
Admission routes1
Has abstractyes

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