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Record W2899860682 · doi:10.1098/rspb.2018.1971

Intraspecific brain size variation between coexisting sunfish ecotypes

2018· article· en· W2899860682 on OpenAlexafffund
Caleb J. Axelrod, Frédéric Laberge, Beren W. Robinson

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsPelagic zoneLittoral zoneIntraspecific competitionEcologyForagingEcotypeHabitatBiologyBrain sizePopulationLepomisPredation

Abstract

fetched live from OpenAlex

Variation in spatial complexity and foraging requirements between habitats can impose different cognitive demands on animals that may influence brain size. However, the relationship between ecologically related cognitive performance and brain size is not well established. We test whether variation in relative brain size and brain region size is associated with habitat use within a population of pumpkinseed sunfish composed of different ecotypes that inhabit either the structurally complex shoreline littoral habitat or simpler open-water pelagic habitat. Sunfish using the littoral habitat have on average 8.3% larger brains than those using the pelagic habitat. We found little difference in the proportional sizes of five brain regions between ecotypes. The results suggest that cognitive demands on sunfish may be reduced in the pelagic habitat given no habitat-specific differences in body condition. They also suggest that either a short divergence time or physiological processes may constrain changes to concerted, global modifications of brain size between sunfish ecotypes.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient 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 score0.040
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.035
GPT teacher head0.264
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; 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

Citations47
Published2018
Admission routes2
Has abstractyes

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