{"id":"W2294110607","doi":"","title":"Adaptive Identification of Landmine Class by Evaluating the Total Degree of Conformity of Ring-CSOM Weights in a Ground Penetrating Radar System","year":2010,"lang":"en","type":"article","venue":"International Conference on Intelligent Information Processing","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Ground-penetrating radar; Radar; Artificial intelligence; Feature (linguistics); Identification (biology); Remote sensing; Computer science; Feature extraction; Class (philosophy); Feature vector; Computer vision; Noise (video); Geology; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005375376,0.0001146305,0.0001719349,0.0001389452,0.00004568994,0.00005214415,0.0002727424,0.00006426009,0.00002149854],"category_scores_gemma":[0.0001119667,0.0000921824,0.00004679898,0.0001913177,0.00007617541,0.0005388273,0.00002800139,0.0002350507,0.000005385207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004882952,"about_ca_system_score_gemma":0.00005983022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000709033,"about_ca_topic_score_gemma":0.00001116163,"domain_scores_codex":[0.9984154,0.0000252842,0.0009686445,0.00008587501,0.0004044063,0.0001004302],"domain_scores_gemma":[0.9985168,0.0001447286,0.0005817282,0.000142432,0.0005889551,0.00002536136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009024868,0.0001020367,0.0002458685,0.0006408244,0.00005672459,8.805205e-8,0.004311247,0.004366912,0.5426409,0.2411866,0.000006510304,0.2063521],"study_design_scores_gemma":[0.0002661869,0.00008758254,0.006720183,0.0004496078,0.00001472465,0.000003915433,0.00280144,0.8385319,0.1491382,0.001814743,0.00003459502,0.0001370294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9248304,0.00001478123,0.06608413,0.0000509963,0.0002055418,0.0002787205,0.00006623668,0.00002902257,0.00844021],"genre_scores_gemma":[0.9973443,0.000005472598,0.002500143,0.000005091265,0.00002376122,0.0000446507,0.00005083876,0.000005903343,0.00001985103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8341649,"threshold_uncertainty_score":0.3759089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05922126072996002,"score_gpt":0.3256434527137927,"score_spread":0.2664221919838327,"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."}}