{"id":"W1988550220","doi":"10.1117/12.503905","title":"&lt;title&gt;EM-ML algorithm for track initialization with features using possibly noninformative data&lt;/title&gt;","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Initialization; Computer science; Track (disk drive); Algorithm; Programming language; Operating 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":[0.0004395451,0.001100284,0.0008632734,0.0006614809,0.0003807296,0.0009901201,0.001473601,0.001031944,0.03555471],"category_scores_gemma":[0.001637946,0.0003443826,0.0005123038,0.001172237,0.0004075378,0.001258348,0.0007951622,0.001458372,0.03155308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005551677,"about_ca_system_score_gemma":0.0005506218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004266761,"about_ca_topic_score_gemma":0.005043339,"domain_scores_codex":[0.999617,0.00006822778,0.00002301439,0.0001145882,0.0001425462,0.00003461892],"domain_scores_gemma":[0.9994926,0.0001589332,0.00004570968,0.0001031532,0.0001810596,0.00001834452],"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":[0.0003925701,0.00007333331,0.0005249077,0.0003385607,0.00004809646,0.0002581227,0.00006717773,0.06794822,0.02715334,0.02024231,0.1304333,0.7525201],"study_design_scores_gemma":[0.0000408444,0.0001130738,0.0007709045,0.00006197987,0.00002253625,0.0001890635,0.00002213795,0.8495626,0.02493574,0.0103937,0.1138286,0.00005892637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002343936,0.000652484,0.9799488,0.0003611635,0.000419292,0.0001077278,0.0005958879,0.006439439,0.009131226],"genre_scores_gemma":[0.06178848,0.001371267,0.8407547,0.0005644481,0.0004486358,0.0004067335,0.006175116,0.003180171,0.08531048],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03555471,"threshold_uncertainty_score":0.1189423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01957001185183254,"score_gpt":0.2610832954590925,"score_spread":0.2415132836072599,"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."}}