{"id":"W2431198722","doi":"10.1109/irs.2016.7497347","title":"Design of an IMM-NNJPDA tracker for HFSWR","year":2016,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"BitTorrent tracker; Tracking (education); Radar; Flexibility (engineering); Radar tracker; Computer science; Set (abstract data type); Artificial intelligence; Engineering; Telecommunications; Mathematics","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":[0.0003387749,0.00008650393,0.0001226367,0.00004554515,0.00005215482,0.00003701299,0.0006602971,0.00005807179,0.00008420653],"category_scores_gemma":[0.00003683001,0.00005075426,0.00003937537,0.0001081484,0.00003510818,0.0004699269,0.00006440304,0.00002854011,0.00003170182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007227436,"about_ca_system_score_gemma":0.00002628546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007266572,"about_ca_topic_score_gemma":0.000001793419,"domain_scores_codex":[0.9991046,0.00004944436,0.0002046727,0.0002850924,0.000140698,0.0002155532],"domain_scores_gemma":[0.9988059,0.0003654696,0.00006301747,0.0006004566,0.00008971505,0.00007546218],"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":[0.00008648681,0.00024298,0.0001515164,0.00001603392,0.00002496839,0.000006092842,0.0002884169,0.0007049032,0.05013215,0.1383426,0.05240319,0.7576006],"study_design_scores_gemma":[0.005481696,0.002254912,0.002916781,0.0002004657,0.00003589113,0.00007497946,0.00008311788,0.4587748,0.3526601,0.08097467,0.09508263,0.001459919],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003593005,0.00002460522,0.9948145,0.0004075316,0.0003263087,0.000153802,0.000008207006,0.0001484963,0.0005235721],"genre_scores_gemma":[0.3529089,0.00001361449,0.6456226,0.0002126093,0.00009620326,0.00001410513,0.000002688948,0.000009594565,0.001119711],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7561407,"threshold_uncertainty_score":0.2069699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03492047382573842,"score_gpt":0.2618546848641178,"score_spread":0.2269342110383794,"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."}}