{"id":"W3094289000","doi":"10.1101/2020.10.21.340968","title":"Automated Feature Extraction from Large Cardiac Electrophysiological Data Sets","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Aurora Research Institute","keywords":"Electrophysiology; Computer science; Pattern recognition (psychology); Segmentation; Cardiac electrophysiology; Artificial intelligence; Action (physics); Set (abstract data type); Feature extraction; Feature (linguistics); Neuroscience; Biology; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007462829,0.001023458,0.0009074633,0.003038307,0.0003361487,0.001118923,0.0006112491,0.0007619802,0.0009794328],"category_scores_gemma":[0.003337254,0.0002388637,0.0007123703,0.001876689,0.0003789017,0.0006884066,0.0008664742,0.000832202,0.0007140798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003209004,"about_ca_system_score_gemma":0.0004629247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001158086,"about_ca_topic_score_gemma":0.001081634,"domain_scores_codex":[0.9991105,0.0001414416,0.0001070234,0.0003074443,0.0002473567,0.0000860763],"domain_scores_gemma":[0.9972935,0.001605328,0.0002495901,0.0003197565,0.0004692293,0.00006256325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003396839,0.00038299,0.008092641,0.0003374653,0.0001552086,0.000868508,0.0002069686,0.05817414,0.1928815,0.001208774,0.006557828,0.7307942],"study_design_scores_gemma":[0.00003700313,0.0001765277,0.03059038,0.00003817154,0.00005441795,0.0006193747,0.0001817067,0.90285,0.05510403,0.006738594,0.003542662,0.00006707216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2495142,0.0007026538,0.7387599,0.0004448855,0.0001130786,0.0001816476,0.004219109,0.005262456,0.0008021184],"genre_scores_gemma":[0.631864,0.0003362688,0.3565768,0.00008490665,0.0001340964,0.0003570077,0.009414053,0.0002312448,0.001001727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003038307,"threshold_uncertainty_score":0.003946781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03992822329951294,"score_gpt":0.272165964316272,"score_spread":0.2322377410167591,"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."}}