{"id":"W2230206282","doi":"10.1049/iet-rsn.2015.0098","title":"Parametric texture estimation and prediction using measured sea clutter data","year":2015,"lang":"en","type":"article","venue":"IET Radar Sonar & Navigation","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Research Council; Office of Science; Asian Office of Aerospace Research and Development","keywords":"Clutter; Parametric statistics; Texture (cosmology); Estimation; Parametric model; Artificial intelligence; Computer science; Semiparametric model; Pattern recognition (psychology); Statistics; Mathematics; Engineering; Radar; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009092474,0.0001165004,0.0001140997,0.0001345942,0.0001593376,0.0001780511,0.0002098533,0.0001136306,0.00005047615],"category_scores_gemma":[0.0001204479,0.0001037973,0.00001333999,0.0004593255,0.00007283066,0.0009019648,0.00003656663,0.0002040165,0.00006263077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003267223,"about_ca_system_score_gemma":0.0001289391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001604532,"about_ca_topic_score_gemma":0.0001847224,"domain_scores_codex":[0.9983425,0.0001418591,0.0001969954,0.0003415808,0.0007431459,0.0002339858],"domain_scores_gemma":[0.9992295,0.0000776219,0.00007320371,0.000307403,0.0001290354,0.0001832396],"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":[0.000146238,0.00004900817,0.3998681,0.0001034017,0.0000622122,0.00003431606,0.0009812423,0.1806618,0.0007200399,0.000004003904,0.006224986,0.4111447],"study_design_scores_gemma":[0.0003437389,0.00007060773,0.04332814,0.00002630325,0.00003048688,0.0000601393,0.0000818705,0.9538531,0.0001241625,0.000952909,0.001022218,0.0001063284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7688981,0.0006337552,0.2282044,0.0004144315,0.0003104132,0.0004431921,0.0007390944,0.00009882355,0.0002578245],"genre_scores_gemma":[0.955052,0.00001628376,0.04116467,0.00004131535,0.0001601603,6.239712e-7,0.003528441,0.000006295378,0.00003024229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7731913,"threshold_uncertainty_score":0.4232732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1171984223710692,"score_gpt":0.308117785462899,"score_spread":0.1909193630918297,"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."}}