{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01331801,0.001184605,0.0009038688,0.001592874,0.0007417218,0.001396562,0.001290427,0.0008485285,0.001053826],"category_scores_gemma":[0.0199139,0.0003061319,0.0008958525,0.001629458,0.0006316253,0.001999461,0.002218954,0.0008096487,0.0005671973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001229412,"about_ca_system_score_gemma":0.001397374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03286114,"about_ca_topic_score_gemma":0.042066,"domain_scores_codex":[0.9943839,0.001823155,0.0003728544,0.000932241,0.002105862,0.0003820198],"domain_scores_gemma":[0.9912733,0.003028873,0.0005379181,0.001802267,0.003063806,0.0002938001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004597084,0.0007058128,0.09017117,0.0006009366,0.001486992,0.0003281138,0.0006224949,0.4702171,0.03507215,0.002647054,0.01148214,0.3820689],"study_design_scores_gemma":[0.0003536697,0.001836066,0.115112,0.00008404663,0.0003157942,0.0001817179,0.0006693567,0.8412347,0.03061621,0.002027707,0.007420911,0.0001477979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8878429,0.001029658,0.09260105,0.0006696786,0.0001789961,0.0005307968,0.006002697,0.004849278,0.006295018],"genre_scores_gemma":[0.9266747,0.000150376,0.05728435,0.000134319,0.00002571163,0.0001345122,0.01444525,0.0001840973,0.0009667195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03286114,"threshold_uncertainty_score":0.07043326,"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."}}