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Record W4395470205 · doi:10.1093/conphys/coae013

Incorporating otolith-isotope inferred field metabolic rate into conservation strategies

2024· article· en· W4395470205 on OpenAlexafffund
Valesca A de Groot, Clive N. Trueman, Amanda E. Bates

Bibliographic record

VenueConservation Physiology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of VictoriaMemorial University of Newfoundland
FundersNatural Environment Research CouncilCanadian Society of ZoologistsCompany of BiologistsSight Research UKNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsOtolithBiologyMetabolic rateMultidisciplinary approachEcosystemEcologyEnvironmental resource managementFish <Actinopterygii>Environmental scienceFishery

Abstract

fetched live from OpenAlex

Abstract Fluctuating ocean conditions are rearranging whole networks of marine communities—from individual-level physiological thresholds to ecosystem function. Physiological studies support predictions from individual-level responses (biochemical, cellular, tissue, respiratory potential) based on laboratory experiments. The otolith-isotope method of recovering field metabolic rate has recently filled a gap for the bony fishes, linking otolith stable isotope composition to in situ oxygen consumption and experienced temperature estimates. Here, we review the otolith-isotope method focusing on the biochemical and physiological processes that yield estimates of field metabolic rate. We identify a multidisciplinary pathway in the application of this method, providing concrete research goals (field, modeling) aimed at linking individual-level physiological data to higher levels of biological organization. We hope that this review will provide researchers with a transdisciplinary ‘roadmap’, guiding the use of the otolith-isotope method to bridge the gap between individual-level physiology, observational field studies, and modeling efforts, while ensuring that in situ data is central in marine policy-making aimed at mitigating climatic and anthropogenic threats.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.997

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.284
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.

Study designNot applicable
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

Citations13
Published2024
Admission routes2
Has abstractyes

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