{"id":"W2055932828","doi":"10.1049/iet-rsn.2009.0122","title":"Inference of a generalised texture for a compound – Gaussian clutter","year":2010,"lang":"en","type":"article","venue":"IET Radar Sonar & Navigation","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Clutter; Texture (cosmology); Context (archaeology); Inference; Gaussian; Representation (politics); Computer science; Amplitude; Stochastic process; Phase (matter); Radar cross-section; Range (aeronautics); Gaussian process; Radar; Statistical physics; Algorithm; Pattern recognition (psychology); Field (mathematics); Artificial intelligence; Mathematics; Physics; Statistics; Geology; Optics; Engineering; Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003257462,0.0005028443,0.0009534977,0.001212812,0.0003496124,0.001725192,0.00107212,0.001333005,0.0009733017],"category_scores_gemma":[0.01329131,0.0006450846,0.001159922,0.0008494965,0.002013314,0.001739645,0.001254594,0.001214936,0.0002655033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009664439,"about_ca_system_score_gemma":0.000699028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00306393,"about_ca_topic_score_gemma":0.002616761,"domain_scores_codex":[0.9990911,0.0002005095,0.00004857205,0.0003055641,0.0002029735,0.0001511915],"domain_scores_gemma":[0.9929439,0.005123647,0.0006055988,0.0007410832,0.0004338704,0.0001519555],"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":[0.0008069348,0.00007350053,0.009330737,0.0001989639,0.0002335553,0.000643156,0.000376546,0.7675733,0.03762777,0.08253055,0.000935648,0.09966937],"study_design_scores_gemma":[0.00001555047,0.00004435263,0.001789549,0.000008989419,0.00001771494,0.0001621744,0.00001969458,0.9725562,0.003238348,0.02180981,0.0003049159,0.00003260278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0542766,0.00006061986,0.9449579,0.00008177591,0.00001691394,0.00001591587,0.00006751937,0.0001333833,0.0003894672],"genre_scores_gemma":[0.7766277,0.000168385,0.2212954,0.0001100073,0.00006299057,0.00003162885,0.0002514239,0.00008064743,0.001371845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003257462,"threshold_uncertainty_score":0.01722735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01031695699126338,"score_gpt":0.2469903456788556,"score_spread":0.2366733886875923,"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."}}