{"id":"W2755293500","doi":"10.1038/s41598-017-10996-6","title":"Grip force when reaching with target uncertainty provides evidence for motor optimization over averaging","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Wellcome Trust; Canadian Institutes of Health Research; Royal Society; Ontario Innovation Trust","keywords":"Computer science; Physical medicine and rehabilitation; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00145172,0.0001621336,0.0001716567,0.0001015094,0.002074052,0.001765214,0.0003208478,0.00004270739,0.00003756582],"category_scores_gemma":[0.004443972,0.0001248355,0.00007941099,0.00008042434,0.000235248,0.001937613,0.00007532146,0.00009225644,0.000002401348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000818724,"about_ca_system_score_gemma":0.0001810728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000128678,"about_ca_topic_score_gemma":0.00003237044,"domain_scores_codex":[0.9976353,0.00005847623,0.0003605999,0.001010284,0.00058599,0.0003493385],"domain_scores_gemma":[0.9976891,0.0001896948,0.0007892312,0.001053191,0.0001724255,0.0001063672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003649959,0.00008366597,0.006107615,0.0001668475,0.00001404197,0.0002373941,0.001724806,0.3156103,0.6674479,0.0009769327,0.001515465,0.005749994],"study_design_scores_gemma":[0.0005914449,0.0001288076,0.001881365,0.0004095567,0.00003667899,0.00009946372,0.00004143404,0.9412895,0.03086181,0.01412288,0.01012535,0.0004117219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4412489,0.00009686309,0.5442952,0.001278648,0.007609213,0.003356306,0.00001510965,0.0002654156,0.001834325],"genre_scores_gemma":[0.9782605,0.000002295586,0.0126515,0.0001117438,0.0001374308,0.0001378065,0.0000123405,0.00002119428,0.00866516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6365861,"threshold_uncertainty_score":0.999271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06007514164095379,"score_gpt":0.2940049844589189,"score_spread":0.2339298428179651,"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."}}