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Record W4291897102 · doi:10.1139/cjpp-2022-0262

In Memoriam: Professor Richard B. Stein (1940–2020) harnessing insights from the neurophysiology of motor control—from bench to bedside

2022· editorial· en· W4291897102 on OpenAlexaffvenue
Tessa Gordon, Dirk G. Everaert, K. Ming Chan

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2022
Typeeditorial
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of AlbertaUniversity of Toronto
Fundersnot available
KeywordsBench to bedsideNeurophysiologyMotor controlNeuroscienceMovement controlSpinal cordCognitive scienceNeurological rehabilitationSpinal cord injuryPsychologyPhysical medicine and rehabilitationMedicinePsychoanalysisRehabilitation

Abstract

fetched live from OpenAlex

The role of afferent feedback and central motor drive in muscle activation has a profound impact on our understanding of movement control in health and disease. Dr. Richard B. Stein was a pioneer who made major contributions to the field. In addition to fundamental discoveries using animal models, he translated this to the clinic to benefit patients with spinal cord and other neurological injuries. Along the way, he inspired a generation of scientists around the world.

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)0.006
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.014
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.025
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.001
Science and technology studies0.0030.003
Scholarly communication0.0070.006
Open science0.0040.002
Research integrity0.0140.028
Insufficient payload (model declined to judge)0.0110.017

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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations1
Published2022
Admission routes2
Has abstractyes

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