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Record W2192224112 · doi:10.1890/14-1954.1

Exploring the role of temperature in the ocean through metabolic scaling

2015· article· en· W2192224112 on OpenAlexaff
John F. Bruno, Lindsey A. Carr, Mary I. O’Connor

Bibliographic record

VenueEcology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of British Columbia
FundersStrong
KeywordsEcologyBiological dispersalEcosystemBiomass (ecology)Food webPopulationBiology

Abstract

fetched live from OpenAlex

Temperature imposes a constraint on the rates and outcomes of ecological processes that determine community- and ecosystem-level patterns. The application of metabolic scaling theory has advanced our understanding of the influence of temperature on pattern and process in marine communities. Metabolic scaling theory uses the fundamental and ubiquitous patterns of temperature-dependent metabolism to predict how environmental temperature influences patterns and processes at higher levels of biological organization. Here, we outline some of these predictions to review recent advances and illustrate how scaling theory might be applied to new challenges. For example, warming can alter species interactions and food-web structure and can also reduce total animal biomass supportable by a given amount of primary production by increasing animal metabolism and energetic demand. Additionally, within a species, larval development is faster in warmer water, potentially influencing dispersal and other demographic processes like population connectivity and gene flow. These predictions can be extended further to address major questions in marine ecology, and present an opportunity for conceptual unification of marine ecological research across levels of biological organization. Drawing on work by ecologists and oceanographers over the last century, a metabolic scaling approach represents a promising way forward for applying ecological understanding to basic questions as well as conservation challenges.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.073

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.057
GPT teacher head0.239
Teacher spread0.182 · 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 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

Citations98
Published2015
Admission routes1
Has abstractyes

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