{"id":"W2626479176","doi":"10.1109/tbme.2017.2716365","title":"eCurves: A Temporal Shape Encoding","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Congenital Heart Disease Studies","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Center for Research Resources; National Heart, Lung, and Blood Institute","keywords":"Eigenfunction; Eigenvalues and eigenvectors; Artificial intelligence; Pattern recognition (psychology); Encoding (memory); Metric (unit); Mathematics; Computer science; Algorithm; Physics","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":[0.0005334418,0.0004819765,0.0003763752,0.001265528,0.0002115829,0.001001666,0.000917417,0.0006260072,0.002873223],"category_scores_gemma":[0.002879021,0.000278564,0.000739077,0.001128041,0.0003853835,0.001543995,0.001132704,0.000825613,0.001139223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004201268,"about_ca_system_score_gemma":0.0005138348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002132992,"about_ca_topic_score_gemma":0.002911187,"domain_scores_codex":[0.9996494,0.00004664539,0.00002402015,0.00009420346,0.000150072,0.00003561641],"domain_scores_gemma":[0.999266,0.0001783505,0.0001164062,0.0002044904,0.0001796719,0.00005516802],"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":[0.0005368445,0.0001399509,0.003568955,0.0001282577,0.00005828824,0.0002185477,0.0001441642,0.05690917,0.03300637,0.01913822,0.01092232,0.8752289],"study_design_scores_gemma":[0.00003247832,0.0002306587,0.004023924,0.00007879699,0.00004695729,0.0007402981,0.0001096427,0.9089751,0.02761241,0.02694409,0.03113927,0.00006643394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02125009,0.0007673166,0.9709274,0.0003111311,0.000179482,0.00005767825,0.001521397,0.002841516,0.002143882],"genre_scores_gemma":[0.3156912,0.001617857,0.6700943,0.0004767427,0.0003134733,0.00020112,0.004337294,0.0008366657,0.00643126],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002873223,"threshold_uncertainty_score":0.009611845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02390850614112365,"score_gpt":0.2838765925006905,"score_spread":0.2599680863595669,"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."}}