{"id":"W2803718659","doi":"10.1016/j.pneurobio.2019.01.008","title":"The roles of supervised machine learning in systems neuroscience","year":2019,"lang":"en","type":"review","venue":"Progress in Neurobiology","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":155,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"National Institute of Neurological Disorders and Stroke; National Eye Institute","keywords":"Systems neuroscience; Toolbox; Neuroscience; Computational neuroscience; Computer science; Cognitive science; Artificial intelligence; Machine learning; Psychology; Central nervous system","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":[0.001927499,0.001226372,0.001671791,0.002004645,0.0002984611,0.001765762,0.001442475,0.001734318,0.002756128],"category_scores_gemma":[0.003174877,0.0003952898,0.0005247041,0.003177344,0.002254375,0.002821883,0.001188478,0.00367227,0.001557983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001433528,"about_ca_system_score_gemma":0.001772165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001670837,"about_ca_topic_score_gemma":0.001782609,"domain_scores_codex":[0.9995042,0.0001317723,0.00005024087,0.00009348869,0.0001930645,0.00002719193],"domain_scores_gemma":[0.9972515,0.001990112,0.0001091088,0.00009376263,0.0004620906,0.00009341267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006015993,0.00006656152,0.0001789304,0.009266991,0.0001416243,0.00007580778,0.00005717264,0.003333673,0.0008964859,0.05005046,0.03596882,0.8999034],"study_design_scores_gemma":[0.00002811728,0.00009724279,0.0008982954,0.003932604,0.000114429,0.0004609813,0.00005979247,0.003527537,0.0007649424,0.108066,0.8819902,0.00005986157],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00009520072,0.99177,0.004262741,0.001049398,0.0007122814,0.000004855457,0.00002548708,0.00002309648,0.00205698],"genre_scores_gemma":[0.002013092,0.9920331,0.002832538,0.0004916505,0.001639936,0.00001226497,0.00004217301,0.00001084294,0.0009244409],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002756128,"threshold_uncertainty_score":0.01040095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06991791918017289,"score_gpt":0.3274390572498681,"score_spread":0.2575211380696952,"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."}}