{"id":"W2991391187","doi":"10.1371/journal.pbio.3000516","title":"Data-driven analyses of motor impairments in animal models of neurological disorders","year":2019,"lang":"en","type":"article","venue":"PLoS Biology","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"Institute of Neurosciences, Mental Health and Addiction; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Stroke (engine); Categorical variable; Physical medicine and rehabilitation; Movement disorders; Artificial neural network; Neuroscience; Machine learning; Disease; Neurological disorder; Artificial intelligence; Computer science; Biology; Central nervous system disease; Medicine; Pathology","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":[],"consensus_categories":[],"category_scores_codex":[0.00006814959,0.00006054847,0.0003290041,0.000130031,0.000003378544,4.703969e-7,0.0001138021,0.00008782254,0.0001253297],"category_scores_gemma":[0.0001214218,0.00004109232,0.00006033507,0.00009368449,0.00009331449,0.00003758526,0.00007751707,0.00007667569,0.000007710561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006500034,"about_ca_system_score_gemma":0.00003119266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004701268,"about_ca_topic_score_gemma":0.000008811391,"domain_scores_codex":[0.9993158,0.00007812742,0.000244865,0.0001948023,0.00006029663,0.000106085],"domain_scores_gemma":[0.9994215,0.0001816658,0.0000767181,0.0002655728,0.00002714153,0.00002743969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004544844,0.0004235164,0.837667,0.00006819483,0.00006104815,0.000001252247,0.00003752091,0.0001037899,0.1608336,0.00008128804,0.00001835921,0.0002499861],"study_design_scores_gemma":[0.00158747,0.004904551,0.9323632,0.0000299472,0.00004507834,0.000004017046,0.00008378024,0.05971566,0.0007117116,0.0003910314,0.0001003187,0.0000632275],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988155,0.0001359258,0.00002451803,0.0002360248,0.00005017744,0.0002673366,0.00009129784,0.000008090062,0.0003711278],"genre_scores_gemma":[0.9990843,0.00008621747,0.0006353796,0.00007604018,0.00001231887,0.000004202552,0.000072855,0.000004165514,0.00002452999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1601218,"threshold_uncertainty_score":0.1675696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07700783573551956,"score_gpt":0.3554306548762196,"score_spread":0.2784228191407001,"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."}}