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Record W4382513839 · doi:10.1016/j.actao.2023.103917

Correlation between body size and longevity: New analysis and data covering six taxonomic classes of vertebrates

2023· article· en· W4382513839 on OpenAlexafffund
Anna Kuparinen, Emily J. Yeung, Jeffrey A. Hutchings

Bibliographic record

VenueActa Oecologica · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsDalhousie University
FundersH2020 European Research CouncilEuropean Research CouncilEuropean CommissionNatural Sciences and Engineering Research Council of CanadaHorizon 2020Academy of Finland
KeywordsBiologyLongevityTaxonPhylogenetic treeChondrichthyesTaxonomic rankPhylogenetic comparative methodsZoologyEcologyEvolutionary biology

Abstract

fetched live from OpenAlex

Large bodied species are known to live longer than small bodied species. However, it is less clear whether the positive correlation varies across taxa. In this short communication, we combine data entries from literature and databases on body mass and maximum life span for 3722 species covering taxonomic Classes Chondrichthyes, Teleostei, Amphibia, Reptilia, Aves, and Mammalia. We then analyse the log(maximum life span) – log(body mass) relationship using generalized linear model with nested random intercepts and slopes for Class/Order/Family. Our analyses generally demonstrate the positive longevity – body mass relationship but also reveal that slopes and intercepts differ slightly among all Classes except Reptilia and Amphibia. Highest slopes can be found in Classes Aves and Chondrichthyes. Differences between the smallest and largest Family-level slopes was more than threefold. While these preliminary analyses provide a brief overview of body size – longevity relationships across taxa, the dataset collated in the present study could serve as a start point for in-depth phylogenetic analyses to uncover complex pathways through which body size and its correlates might have evolved.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score1.000

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)0.0010.000

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 head0.027
GPT teacher head0.265
Teacher spread0.238 · 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 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

Citations18
Published2023
Admission routes2
Has abstractyes

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