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Record W4402666476 · doi:10.1093/evolut/qpae137

Does brain size of Asiatic toads (<i>Bufo gargarizans</i>) trade-off with other energetically expensive organs along altitudinal gradients?

2024· article· en· W4402666476 on OpenAlexafffund
Zhongyi Yao, Kun Huang, Yin Qi, Jinzhong Fu

Bibliographic record

VenueEvolution · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBiologyAltitude (triangle)EcologyEffects of high altitude on humansBufoZoologyPhysiologyAnatomyToad

Abstract

fetched live from OpenAlex

Brain size variation is often attributed to energetic trade-offs with other metabolically expensive tissues and organs, which is a prediction of the expensive brain hypothesis (EBH). Here we examine Asiatic toads (Bufo gargarizans) along altitudinal gradients and test size trade-offs between the brain and four visceral organs (heart, liver, alimentary tract, and kidney) with altitude. Body size and scaled mass index (a proxy for total energy intake) decline with altitude, implying stronger energetic constraints at high altitudes. Relative brain size decreases along altitudinal gradients, while visceral organs mostly increase in relative sizes. Using structural equation modeling, a significant negative relationship between brain size and a latent variable "budget," which represents the energy allocation to the four visceral organs, is detected among high-altitudinal toads. Heart appears to have the largest and most consistent response to changes in energy allocation. No such relationships are observed among toads at middle- and low-altitudes, where high energy intake may allow individuals to forego energetic trade-offs. When applying EBH to poikilotherms, a great emphasis should be placed on total energy intake in addition to energy allocation. Future research on EBH will benefit from more intra-specific comparisons and the evaluation of fitness consequences beyond energy limitation.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.964

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 science0.0000.000
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 head0.005
GPT teacher head0.203
Teacher spread0.198 · 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 designBench or experimental
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

Citations4
Published2024
Admission routes2
Has abstractyes

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