{"id":"W2951195401","doi":"10.48550/arxiv.1805.08239","title":"The Roles of Supervised Machine Learning in Systems Neuroscience","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Toolbox; Systems neuroscience; Computational neuroscience; Computer science; Neuroscience; Artificial intelligence; Machine learning; Cognitive science; 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":[0.01010275,0.0009354532,0.001249961,0.001961675,0.001207528,0.004295764,0.001339141,0.002493273,0.002402031],"category_scores_gemma":[0.01753086,0.0004913407,0.0005495672,0.001482118,0.01104347,0.00856273,0.002770473,0.006042328,0.0008810502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002249171,"about_ca_system_score_gemma":0.002174866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001183516,"about_ca_topic_score_gemma":0.0008344428,"domain_scores_codex":[0.9950989,0.003052415,0.0002146523,0.0005569972,0.0009503085,0.0001267264],"domain_scores_gemma":[0.9787022,0.01766009,0.0004047798,0.001388896,0.001437021,0.0004071065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002587146,0.00002668416,0.000594999,0.0004515119,0.00003830773,0.00004076414,0.0002267695,0.007103174,0.0002417792,0.9139435,0.006426992,0.07087964],"study_design_scores_gemma":[0.000004723194,0.00001284985,0.000168626,0.0001362413,0.000004730055,0.00002777866,0.00003246661,0.01036682,0.0001487531,0.9751932,0.01388878,0.00001495219],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.009324883,0.2540242,0.5846847,0.08748302,0.003126178,0.0000824594,0.0003054514,0.0006153726,0.06035375],"genre_scores_gemma":[0.482848,0.2108891,0.2695328,0.009728139,0.01822762,0.0004076945,0.0003971329,0.0003150242,0.007654372],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01010275,"threshold_uncertainty_score":0.05342907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06087281412528758,"score_gpt":0.1886398697415559,"score_spread":0.1277670556162683,"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."}}