{"id":"W2146929719","doi":"10.1109/cdc.2010.5717202","title":"Mode Detection in switched pursuit tracking tasks: Hybrid estimation to measure performance in Parkinson's disease","year":2010,"lang":"en","type":"article","venue":"","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Task (project management); Computer science; Mode (computer interface); Tracking (education); Cursor (databases); Cognition; Tracking error; Parkinson's disease; Control theory (sociology); Artificial intelligence; Psychology; Disease; Control (management); Neuroscience; Medicine; Engineering; Human–computer interaction","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":[0.0008248247,0.0004319826,0.0003955094,0.0005955694,0.0001117141,0.000310524,0.0001930257,0.0004967009,0.0004530697],"category_scores_gemma":[0.004567204,0.0001696333,0.0001992251,0.0003264697,0.0001584752,0.0003298321,0.0003429078,0.0002379704,0.0001221894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001840992,"about_ca_system_score_gemma":0.0001118351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001240078,"about_ca_topic_score_gemma":0.00117408,"domain_scores_codex":[0.9995807,0.0001568135,0.00004575094,0.00009051168,0.00009949469,0.00002678686],"domain_scores_gemma":[0.9982539,0.001111783,0.0002733818,0.0001086845,0.0001587126,0.00009360213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009123014,0.001736172,0.4285649,0.0004961016,0.0007802602,0.0003379982,0.001424071,0.05285972,0.2600068,0.0004050999,0.0003614156,0.2439046],"study_design_scores_gemma":[0.0001127403,0.004889699,0.7590271,0.0000208281,0.0001609378,0.000857516,0.0001929345,0.2037548,0.03016492,0.0004519044,0.0002864953,0.00008013864],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857054,0.00006884173,0.01376602,0.000008501658,0.000003627606,0.00003800693,0.00007432627,0.00006522847,0.0002700463],"genre_scores_gemma":[0.9941657,0.00002961502,0.005450944,0.000009002645,0.000003044056,0.00003757088,0.0001190925,0.000006916906,0.0001781396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001240078,"threshold_uncertainty_score":0.004362166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02454878863170545,"score_gpt":0.2528548172615807,"score_spread":0.2283060286298753,"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."}}