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Record W4238494866 · doi:10.32942/osf.io/6kcwa

Replication alert: behavioural lateralisation in a detour test is not repeatable in fishes

2019· preprint· en· W4238494866 on OpenAlexaff
Dominique G. Roche, Mirjam Amcoff, Rachael Morgan, Josefin Sundin, Anna H. Andreassen, Mette H. Finnøen, Michael Lawrence, E. N. Henderson, Tommy Norin, Ben Speers‐Roesch

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsUniversity of New BrunswickCarleton University
Fundersnot available
KeywordsHuman multitaskingReplication (statistics)PopulationPsychologyShoaling and schoolingCognitive psychologyBiologyEcologyDemography

Abstract

fetched live from OpenAlex

Behavioural lateralisation, defined as the asymmetric expression of cognitive functions, is reported to enhance key fitness-relevant traits such as predator escape performance, multitasking abilities, and group coordination. Therefore, studies reporting negative effects on lateralisation in fish due to environmental stressors such as ocean acidification, hypoxia, and pollutants are worrisome. However, such studies have focussed on population-level measures, without validating whether lateralisation is consistent within individuals across time. We conducted a multi-species, international assessment of the repeatability (R) of lateralisation in four previously studied fish species using the common detour test, and re-analysed a published dataset (on guppies) using new statistical methods. We expected the three shoaling species to exhibit greater within-individual consistency in lateralisation than their non-shoaling counterparts given previous reports of stronger lateralisation in group-living fishes. However, both absolute and relative lateralisation scores were highly non-repeatable in all five species (0.01

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.095
GPT teacher head0.314
Teacher spread0.219 · 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 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

Citations6
Published2019
Admission routes1
Has abstractyes

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