{"id":"W2131894460","doi":"","title":"An assessment of hierarchical data fusion using SEABAR'07 data","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; General Dynamics (Canada)","funders":"","keywords":"Sonar; Sensor fusion; Computer science; Tracking (education); Artificial intelligence; Fusion; Multistatic radar; Sonar signal processing; Data mining; Radar; Bistatic radar; Signal processing; Radar imaging; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008038951,0.0001757547,0.0001856483,0.000302136,0.0001784565,0.0004509619,0.005176684,0.0001150617,0.0003230778],"category_scores_gemma":[0.0001042339,0.0001608974,0.00002936778,0.0002380908,0.00004666996,0.006792342,0.001346132,0.0003144761,0.00005219652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005585814,"about_ca_system_score_gemma":0.0002357329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005538348,"about_ca_topic_score_gemma":0.000004787167,"domain_scores_codex":[0.9974335,0.0001045237,0.0006910351,0.0004491578,0.001111093,0.0002106258],"domain_scores_gemma":[0.996408,0.00007602738,0.0004091935,0.002543307,0.0004329731,0.0001305039],"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":[0.00008261022,0.0003903918,0.0004765008,0.00001432599,0.00002152621,0.000006597822,0.0005334779,0.004637212,0.004723101,0.4935828,0.005204029,0.4903274],"study_design_scores_gemma":[0.0003884957,0.0001632204,0.006918798,0.0001088994,0.000005149695,0.00001624096,0.00005575673,0.9822488,0.0001351498,0.002857682,0.006922468,0.0001792891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0245126,0.000006328036,0.9579628,0.001921681,0.001274085,0.000202501,0.0007031329,0.0001717376,0.01324513],"genre_scores_gemma":[0.8665578,0.00007819155,0.1265729,0.0007436309,0.0001378746,0.000001154241,0.005892262,0.000004130161,0.00001201844],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9776117,"threshold_uncertainty_score":0.9619653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1177177876449208,"score_gpt":0.4014478166691305,"score_spread":0.2837300290242097,"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."}}