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Record W2605689714 · doi:10.1152/jn.00978.2016

The neural control of interlimb coordination during mammalian locomotion

2017· review· en· W2605689714 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Neurophysiology · 2017
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsNeuroscienceSpinal cordBipedalismCentral pattern generatorSomatosensory systemBiological neural networkBiologyGaitSpinal cord injuryAnatomyPsychologyRhythmPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Neuronal networks within the spinal cord directly control rhythmic movements of the arms/forelimbs and legs/hindlimbs during locomotion in mammals. For an effective locomotion, these networks must be flexibly coordinated to allow for various gait patterns and independent use of the arms/forelimbs. This coordination can be accomplished by mechanisms intrinsic to the spinal cord, somatosensory feedback from the limbs, and various supraspinal pathways. Incomplete spinal cord injury disrupts some of the pathways and structures involved in interlimb coordination, often leading to a disruption in the coordination between the arms/forelimbs and legs/hindlimbs in animal models and in humans. However, experimental spinal lesions in animal models to uncover the mechanisms coordinating the limbs have limitations due to compensatory mechanisms and strategies, redundant systems of control, and plasticity within remaining circuits. The purpose of this review is to provide a general overview and critical discussion of experimental studies that have investigated the neural mechanisms involved in coordinating the arms/forelimbs and legs/hindlimbs during mammalian locomotion.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.109
GPT teacher head0.434
Teacher spread0.324 · 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