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Record W6921411237 · doi:10.6084/m9.figshare.c.7900925

Supplementary material from "Giant mice on small islands: Biogeographic and ecological differences contribute to gigantism in island populations"

2025· other· en· W6921411237 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGigantismMainlandSpecies richnessEndemismInsular biogeographyMediterranean IslandsBiogeographyPredator

Abstract

fetched live from OpenAlex

Island populations of small land vertebrates frequently exhibit insular gigantism, presenting with larger body sizes in comparison to mainland counterparts. While insular gigantism has been observed globally, the effects of biogeographic and ecological factors on body size in island systems are not well understood. Here we examine the biogeographic and ecological associations of insular gigantism. Deer Mice (Peromyscus maniculatus) were live trapped and body mass measured on six of the Gulf Islands and the nearby mainland of British Columbia, Canada. Biogeographic measures of land area and island distance from the mainland and the ecological measure of predator species richness were used in piecewise structural equation modeling to identify associations with insular gigantism. We found evidence of insular gigantism in the Gulf Islands system, with island mice having a larger mean body mass than mainland populations. Land area was positively associated with predator species richness, and predator species richness had a strong negative effect on Deer Mouse body mass, resulting in the observed pattern of insular gigantism. The concurrent analysis of biogeographic and ecological factors contributes to a better understanding of the evolution of insular gigantism in small vertebrates and its juxtaposition to the phenomenon of insular dwarfism of large vertebrates.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.588
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.5210.002

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 head0.036
GPT teacher head0.265
Teacher spread0.229 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2025
Admission routes1
Has abstractyes

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