{"id":"W2807798408","doi":"10.1109/radar.2018.8378789","title":"Heavy-tailed sea clutter modeling for shore-based radar detection","year":2018,"lang":"en","type":"article","venue":"","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Clutter; Rayleigh distribution; Radar; Remote sensing; K-distribution; Shore; Probability distribution; Statistical power; Statistical model; Radar horizon; Computer science; Probability density function; Geology; Radar imaging; Statistics; Continuous-wave radar; Mathematics; Artificial intelligence; Telecommunications","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":[0.000153608,0.0001142931,0.0001266046,0.00006204389,0.0001144651,0.00005877781,0.00006301601,0.00007555586,0.00004133064],"category_scores_gemma":[0.000008529686,0.00009974135,0.00005878058,0.00008495619,0.00001226669,0.0001234804,0.000004819728,0.00006169425,0.00002555706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000518654,"about_ca_system_score_gemma":0.00001406232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003386281,"about_ca_topic_score_gemma":0.00009550859,"domain_scores_codex":[0.9993439,0.00000805815,0.0001895524,0.0001409627,0.00009992387,0.0002176305],"domain_scores_gemma":[0.9997248,0.00001971643,0.00001329954,0.0001178117,0.00007254783,0.00005179737],"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":[0.0003277637,0.00004965681,0.0003003652,0.001146063,0.0001505023,0.000004181284,0.001018135,0.6349389,0.2459448,0.0001296842,0.004830311,0.1111597],"study_design_scores_gemma":[0.0002840316,0.00004787419,0.000003250191,0.00002948422,0.000007325923,0.000001666266,0.00004382561,0.9127831,0.08494041,0.0001122192,0.001613559,0.0001332457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1204055,0.00007697681,0.8768225,0.00005013167,0.0003725752,0.0001671213,0.000002324931,0.0003351843,0.001767661],"genre_scores_gemma":[0.9919617,5.514892e-7,0.007226646,0.0001060841,0.000558627,0.00002413874,0.000003236516,0.00003995321,0.00007903973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8715562,"threshold_uncertainty_score":0.4067334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655619646924465,"score_gpt":0.2227985603897379,"score_spread":0.2062423639204933,"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."}}