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Record W2604277283 · doi:10.1002/ece3.2918

Brain morphology of the threespine stickleback (<i><scp>G</scp>asterosteus aculeatus</i>) varies inconsistently with respect to habitat complexity: A test of the Clever Foraging Hypothesis

2017· article· en· W2604277283 on OpenAlexaboutno aff
Newaz Ahmed, Cole Thompson, Daniel I. Bolnick, Yoel E. Stuart

Bibliographic record

VenueEcology and Evolution · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsForagingGasterosteusSticklebackBiologyCerebrumNeuroanatomyEcologyBrain sizeHabitatSpatial heterogeneityNeuroscienceFish <Actinopterygii>Fishery

Abstract

fetched live from OpenAlex

Abstract The Clever Foraging Hypothesis asserts that organisms living in a more spatially complex environment will have a greater neurological capacity for cognitive processes related to spatial memory, navigation, and foraging. Because the telencephalon is often associated with spatial memory and navigation tasks, this hypothesis predicts a positive association between telencephalon size and environmental complexity. The association between habitat complexity and brain size has been supported by comparative studies across multiple species but has not been widely studied at the within‐species level. We tested for covariation between environmental complexity and neuroanatomy of threespine stickleback (Gasterosteus aculeatus) collected from 15 pairs of lakes and their parapatric streams on Vancouver Island. In most pairs, neuroanatomy differed between the adjoining lake and stream populations. However, the magnitude and direction of this difference were inconsistent between watersheds and did not covary strongly with measures of within‐site environmental heterogeneity. Overall, we find weak support for the Clever Foraging Hypothesis in our study.

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)0.000
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.217
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 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

Citations18
Published2017
Admission routes1
Has abstractyes

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