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Record W4392770360 · doi:10.1111/jfb.15694

Brobdingnagians and Goliaths: two forms of gigantism in fish

2024· article· en· W4392770360 on OpenAlexaff
Daniel Pauly, Elaine Chu, Johannes Müller

Bibliographic record

VenueJournal of Fish Biology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
Fundersnot available
KeywordsGigantismBiologyFish <Actinopterygii>TaxonZoologyPopulationEcologyPredationFisheryDemography

Abstract

fetched live from OpenAlex

Two forms of gigantism are differentiated in fish, Brobdingnagian and Goliathan gigantism, the former applying to populations whose individuals are all larger than is typical for the taxon, the latter to single individuals within a population. While Brobdingnagian gigantism is largely explained by various ecological and evolutionary rules, Goliathan gigantism is not. A mechanistic hypothesis is proposed which explains Goliathan gigantism in terms of the reduction of oxygen requirements of individual fish via moving to cooler temperatures and/or acquiring larger, more energy-dense prey, which enable them to get bigger, and, in the process, sometimes generate bimodal size distributions that may qualify as gradual forms between Goliathan and Brobdingnagian gigantism. This mechanism, which relies on the manner in which their gill surface area grows, is more likely to operate in fish that can get big in the first place than in fish that remain small.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2024
Admission routes1
Has abstractyes

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