{"id":"W1964870877","doi":"10.1109/taes.2014.120672","title":"Joint class identification and target classification using multiple HMMs","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"A priori and a posteriori; Computer science; Identification (biology); Artificial intelligence; Hidden Markov model; Class (philosophy); Pattern recognition (psychology); Feature (linguistics); Machine learning; Joint (building); Tracking (education); Maximization; Data mining; Mathematics; Mathematical optimization; Engineering","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":[0.0005283592,0.0001898512,0.0002195863,0.0001302618,0.0004578977,0.0003667179,0.0001960146,0.0001376282,0.000002558009],"category_scores_gemma":[0.000009554315,0.0001816952,0.00004676908,0.0002677413,0.00006015381,0.0003237659,0.000003710619,0.0002954986,0.00001703434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009814015,"about_ca_system_score_gemma":0.00004028103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001494205,"about_ca_topic_score_gemma":0.00004547722,"domain_scores_codex":[0.9983093,0.0001692339,0.0003255435,0.0005539709,0.0002389047,0.0004030358],"domain_scores_gemma":[0.9989846,0.0001311253,0.0001568611,0.0005382989,0.00006982878,0.0001192348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001785937,0.0008562537,0.002169293,0.0005101176,0.000358115,0.000006066878,0.003374934,0.2848134,0.4739878,0.08608516,0.003798146,0.1438621],"study_design_scores_gemma":[0.0004316213,0.0001107503,0.0006131185,0.00005476462,0.0000190279,0.00005679125,0.0001067309,0.9887451,0.004665426,0.0001808898,0.004781781,0.0002339799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0750995,0.0004619316,0.9228175,0.0003003909,0.000856959,0.0002421196,0.000006325868,0.0001621208,0.00005318273],"genre_scores_gemma":[0.9974124,0.0002746781,0.001777755,0.00006451173,0.00009041229,0.00003361356,0.000004331936,0.00001830902,0.0003239165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.922313,"threshold_uncertainty_score":0.7409317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0230489540968334,"score_gpt":0.2317326051863457,"score_spread":0.2086836510895123,"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."}}