{"id":"W4388363676","doi":"10.1016/j.ibneur.2023.08.1357","title":"SMALL SCALE HETEROGENEITY OF MUA ENABLES HIGH ACCURACY MOVEMENT DECODING","year":2023,"lang":"en","type":"article","venue":"IBRO Neuroscience Reports","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Scale (ratio); Decoding methods; Movement (music); Computer science; Artificial intelligence; Algorithm; Geography; Cartography; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002081975,0.000392963,0.0004649085,0.0002611951,0.000315356,0.0007716748,0.0004559981,0.0004989759,0.002542518],"category_scores_gemma":[0.002140386,0.0003304314,0.0002563049,0.0003946108,0.0004462916,0.0008687347,0.0007392862,0.0006471727,0.0007188626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002969911,"about_ca_system_score_gemma":0.0003682618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006085725,"about_ca_topic_score_gemma":0.001209576,"domain_scores_codex":[0.9998719,0.00002318365,0.000007176037,0.00004217925,0.00003098237,0.00002458619],"domain_scores_gemma":[0.9993085,0.000383535,0.00005119059,0.0001489948,0.00004715907,0.00006062319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004844505,0.000165972,0.003091074,0.000137983,0.00008663147,0.0002933583,0.0001235086,0.03491767,0.8072556,0.01703452,0.002627164,0.1337821],"study_design_scores_gemma":[0.00005453239,0.0002367212,0.01284018,0.00002451628,0.00005734173,0.0005355295,0.0001007695,0.7734314,0.1773501,0.03115953,0.004152087,0.00005722308],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.451167,0.0007497637,0.5308455,0.0007174222,0.0002554864,0.00006512246,0.000429286,0.00234245,0.013428],"genre_scores_gemma":[0.9789785,0.00009398038,0.01947211,0.0000649527,0.0000352608,0.00002295313,0.00008751343,0.0001582988,0.001086474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002542518,"threshold_uncertainty_score":0.008505583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04670617387995377,"score_gpt":0.2865292511277269,"score_spread":0.2398230772477732,"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."}}