{"id":"W1988550220","doi":"10.1117/12.503905","title":"<title>EM-ML algorithm for track initialization with features using possibly noninformative data</title>","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004725363,0.0001835134,0.0002160673,0.00007193866,0.00007137263,0.0001550065,0.0007199172,0.0001207317,0.00001238988],"category_scores_gemma":[0.0001765825,0.0001415759,0.0001631974,0.0002546243,0.00007663792,0.0007294407,0.000100574,0.0001440373,0.000002207497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000623691,"about_ca_system_score_gemma":0.000060717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.477358e-7,"about_ca_topic_score_gemma":6.71966e-8,"domain_scores_codex":[0.9988362,3.429775e-8,0.0002954031,0.0002691763,0.0003677537,0.0002314188],"domain_scores_gemma":[0.998765,0.00006455352,0.0002063118,0.00008138891,0.0008150472,0.00006770571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001017375,0.0000362931,0.000002371491,0.0001228807,0.0001375788,9.638399e-8,0.0002203524,0.0000275783,0.01317491,0.9695414,0.008051777,0.008674579],"study_design_scores_gemma":[0.001486303,0.0004001706,0.00006637674,0.000426632,0.0001987752,0.0001082049,0.0002899419,0.8687984,0.04758863,0.01912439,0.06077975,0.0007324367],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0700024,0.0002181894,0.8925923,0.0004485562,0.000503636,0.000693273,0.0001075712,0.0001197904,0.03531436],"genre_scores_gemma":[0.004520155,0.00003323493,0.9946529,0.00009927565,0.0002059727,0.00002839219,0.00001240369,0.00002934999,0.000418322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.950417,"threshold_uncertainty_score":0.5773298,"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."}}