MétaCan
Menu
Back to cohort
Record W2786493955 · doi:10.1016/j.celrep.2018.01.009

Balance Control Mediated by Vestibular Circuits Directing Limb Extension or Antagonist Muscle Co-activation

2018· article· en· W2786493955 on OpenAlexaff
Andrew Murray, Katherine R. Croce, Timothy Belton, Turgay Akay, Thomas M. Jessell

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsDalhousie University
FundersNational Institutes of HealthGatsby Charitable FoundationWellcome TrustG. Harold and Leila Y. Mathers Charitable FoundationHoward Hughes Medical Institute
KeywordsVestibular systemNeuroscienceBalance (ability)Vestibular nucleiPopulationSensory systemHindlimbMotor neuronPhysical medicine and rehabilitationAnatomyBiologyMedicineSpinal cord

Abstract

fetched live from OpenAlex

Maintaining balance after an external perturbation requires modification of ongoing motor plans and the selection of contextually appropriate muscle activation patterns that respect body and limb position. We have used the vestibular system to generate sensory-evoked transitions in motor programming. In the face of a rapid balance perturbation, the lateral vestibular nucleus (LVN) generates exclusive extensor muscle activation and selective early extension of the hindlimb, followed by the co-activation of extensor and flexor muscle groups. The temporal separation in EMG response to balance perturbation reflects two distinct cell types within the LVN that generate different phases of this motor program. Initially, an LVNextensor population directs an extension movement that reflects connections with extensor, but not flexor, motor neurons. A distinct LVNco-activation population initiates muscle co-activation via the pontine reticular nucleus. Thus, distinct circuits within the LVN generate different elements of a motor program involved in the maintenance of balance.

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)0.003
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 head0.019
GPT teacher head0.257
Teacher spread0.238 · 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

Citations89
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCell ReportsSame topicVestibular and auditory disordersFrench-language works237,207