{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00161855,0.0005608024,0.001032036,0.0008566774,0.000508638,0.001176291,0.001395697,0.001247523,0.001533189],"category_scores_gemma":[0.005372105,0.0005492354,0.000981515,0.001042421,0.000604897,0.002203221,0.001452783,0.00169162,0.001013978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007035191,"about_ca_system_score_gemma":0.0009060273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005042162,"about_ca_topic_score_gemma":0.004464886,"domain_scores_codex":[0.9987146,0.0002935934,0.00008142133,0.0003907437,0.0003343863,0.0001852048],"domain_scores_gemma":[0.9977013,0.001358738,0.0001951968,0.0003873957,0.0002782918,0.00007899762],"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":[0.0005043714,0.00018549,0.006662095,0.0001480419,0.0001561506,0.0002788034,0.0003639108,0.4283346,0.01415172,0.02679947,0.002411075,0.5200042],"study_design_scores_gemma":[0.0000043103,0.00001178014,0.0005146899,0.000004089434,0.000009945844,0.00003858627,0.0000116405,0.9915357,0.002047449,0.005461705,0.0003504983,0.000009619024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01448149,0.0001277887,0.9837754,0.00008831726,0.00003066146,0.0000230231,0.0000696889,0.0006232572,0.0007802423],"genre_scores_gemma":[0.7010453,0.0003808511,0.2929948,0.0001430405,0.00009919688,0.0001276994,0.0005853556,0.0001538068,0.004469951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005042162,"threshold_uncertainty_score":0.01002562,"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."}}