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Record W2167436338 · doi:10.1139/z09-050

Lateral-line activity during undulatory body motions suggests a feedback link in closed-loop control of sea lamprey swimming

2009· article· en· W2167436338 on OpenAlexvenueno aff
Amir Ayali, Simon Gelman, Eric Tytell, Avis H. Cohen

Bibliographic record

VenueCanadian Journal of Zoology · 2009
Typearticle
Languageen
FieldEngineering
TopicBiomimetic flight and propulsion mechanisms
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsPetromyzonLampreyLateral lineBiologyEfferentKinematicsAnatomyVertebrateNeuroscienceZebrafishAfferentFisheryPhysics

Abstract

fetched live from OpenAlex

The lateral-line system is common to most aquatic organisms. It plays an important role in behaviours involving detection of other animals and obstacles. In gnathostome fishes, these behaviours appear to be dependent on an efferent inhibitory system that filters out stimuli caused by the animal’s own movement. Sea lampreys ( Petromyzon marinus L., 1758), the most basal extant vertebrate, possess a functional lateral-line system. Yet they completely lack the inhibitory efferent system. Thus, they may use the lateral line to sense their own swimming movements, helping to stabilize swimming. To test this hypothesis, we first investigated the kinematics of free-swimming lampreys. In an intact tethered preparation, we then generated undualatory body motions of comparable amplitude and frequency to swimming, while monitoring the evoked responses of the posterior lateral-line nerve. Last, we tested the effect of eliminating lateral-line inputs by cobalt treatment. In the tethered preparation, we recorded distinctive and consistent activity in the lateral-line nerve that was strongly dependent on characteristics of the motion. We found that distinct characteristics of the rhythmic movements are encoded in the temporal characteristics of the response. Swimming kinematics of cobalt-treated animals differed from controls, suggesting a complex, yet necessary role of the lateral-line system in closed-loop control of swimming.

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 score0.791
Threshold uncertainty score0.653

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)0.0000.000

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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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

Citations28
Published2009
Admission routes1
Has abstractyes

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