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Record W2746417783 · doi:10.1111/gcb.13831

Sound physiological knowledge and principles in modeling shrinking of fishes under climate change

2017· article· en· W2746417783 on OpenAlexafffund
Daniel Pauly, William W. L. Cheung

Bibliographic record

VenueGlobal Change Biology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFish <Actinopterygii>GillGlobal warmingClimate changeRange (aeronautics)ScalingEcologyMetabolic rateEnvironmental scienceBiologyFisheryAtmospheric sciencesGeologyMathematics

Abstract

fetched live from OpenAlex

One of the main expected responses of marine fishes to ocean warming is decrease in body size, as supported by evidence from empirical data and theoretical modeling. The theoretical underpinning for fish shrinking is that the oxygen supply to large fish size cannot be met by their gills, whose surface area cannot keep up with the oxygen demand by their three-dimensional bodies. However, Lefevre et al. (Global Change Biology, 2017, 23, 3449-3459) argue against such theory. Here, we re-assert, with the Gill-Oxygen Limitation Theory (GOLT), that gills, which must retain the properties of open surfaces because their growth, even while hyperallometric, cannot keep up with the demand of growing three-dimensional bodies. Also, we show that a wide range of biological features of fish and other water-breathing organisms can be understood when gill area limitation is used as an explanation. We also note that an alternative to GOLT, offering a more parsimonious explanation for these features of water-breathers has not been proposed. Available empirical evidence corroborates predictions of decrease in body sizes under ocean warming based on GOLT, with the magnitude of the predicted change increases when using more species-specific parameter values of metabolic scaling.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.323

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 science0.0000.001
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 head0.235
GPT teacher head0.340
Teacher spread0.105 · 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

Citations253
Published2017
Admission routes2
Has abstractyes

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