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Record W2014102907 · doi:10.1242/jeb.013516

Electrocommunication signals in free swimming brown ghost knifefish, <i>Apteronotus leptorhynchus</i>

2008· article· en· W2014102907 on OpenAlexafffund
Ginette J. Hupé, John E. Lewis

Bibliographic record

VenueJournal of Experimental Biology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFish biology, ecology, and behavior
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of OttawaOntario Innovation Trust
KeywordsCommunicationBiologyPsychology

Abstract

fetched live from OpenAlex

Brown ghost knifefish, Apteronotus leptorhynchus, are a species of weakly electric fish that produce a continuous electric organ discharge (EOD) that is used in navigation, prey capture and communication. Stereotyped modulations of EOD frequency and amplitude are common in social situations and are thought to serve as communication signals. Of these modulations, the most commonly studied is the chirp. This study presents a quantitative analysis of chirp production in pairs of free-swimming, physically interacting male and female A. leptorhynchus. Under these conditions, we found that in addition to chirps, the fish commonly produce a second signal type, a type of frequency rise called abrupt frequency rises, AFRs. By quantifying the behaviours associated with signal production, we find that Type 2 chirps tend to be produced when the fish are apart, following periods of low aggression, whereas AFRs tend to be produced when the fish are aggressively attacking one another in close proximity. This study is the first to our knowledge that quantitatively describes both electrocommunication signalling and behavioural correlates on a subsecond time-scale in a wave-type weakly electric fish.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.023
GPT teacher head0.273
Teacher spread0.250 · 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

Citations118
Published2008
Admission routes2
Has abstractyes

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