{"id":"W2986702554","doi":"10.1101/845933","title":"Motor learning and transfer: from feedback to feedforward control","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Feed forward; Stretch reflex; Reflex; Kinematics; Motor control; Control theory (sociology); Elbow; Physical medicine and rehabilitation; Inverse dynamics; Latency (audio); Computer science; Motor learning; Torque; Work (physics); Psychology; Artificial intelligence; Physics; Neuroscience; Engineering; Medicine; Control (management); Control engineering; Anatomy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008559068,0.0005133743,0.0003594412,0.0003333058,0.0001747948,0.00076651,0.0004058522,0.0003983659,0.003547065],"category_scores_gemma":[0.004393682,0.0002496661,0.0002681324,0.0001573989,0.0009422397,0.0008685146,0.001259154,0.0007224989,0.0003724042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003181621,"about_ca_system_score_gemma":0.0003460818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006454496,"about_ca_topic_score_gemma":0.0002060893,"domain_scores_codex":[0.9992748,0.0001981453,0.00004252579,0.0001518314,0.0002309245,0.00010171],"domain_scores_gemma":[0.9986333,0.0006055254,0.0002520865,0.0002063019,0.0001896476,0.0001130521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001276456,0.001070086,0.009481703,0.0006686029,0.0001853203,0.0005031439,0.001052323,0.03252894,0.5270865,0.005157112,0.0004193138,0.4205706],"study_design_scores_gemma":[0.0004536377,0.008777965,0.1512102,0.0004015116,0.0002845889,0.001272619,0.0009267634,0.2676438,0.4974975,0.065711,0.005621667,0.0001986612],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8810752,0.0005147022,0.1107402,0.0002927102,0.00005410955,0.000146848,0.0000520707,0.0005434162,0.006580804],"genre_scores_gemma":[0.995923,0.00006593449,0.003187733,0.00001570974,0.00001009271,0.0000305761,0.00001052588,0.00001302463,0.0007433326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003547065,"threshold_uncertainty_score":0.01186615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01431099982628803,"score_gpt":0.2117355957710167,"score_spread":0.1974245959447286,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}