{"id":"W2013428078","doi":"10.2528/pier12040208","title":"ADAPTIVE DETECTION OF MULTIPLE POINT-LIKE TARGETS UNDER CONIC CONSTRAINTS","year":2012,"lang":"en","type":"article","venue":"Electromagnetic waves","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Natural Science Foundation of China","keywords":"Conic section; Point (geometry); Computer science; Artificial intelligence; Mathematics; Geometry","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":[0.0008856855,0.0005961386,0.0008638165,0.0004191765,0.0001954919,0.0006245248,0.0008557768,0.0007706911,0.0005101804],"category_scores_gemma":[0.004125,0.0002993976,0.0003317366,0.0005747415,0.0006970217,0.0009209079,0.0009331675,0.0006898515,0.0002258776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002979303,"about_ca_system_score_gemma":0.0004976962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000762247,"about_ca_topic_score_gemma":0.001176358,"domain_scores_codex":[0.9990479,0.0002451927,0.00003728131,0.0002403196,0.0003472554,0.00008194761],"domain_scores_gemma":[0.9979235,0.001080739,0.0004124393,0.0002256174,0.0002886692,0.00006897806],"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":[0.0006428768,0.00009375294,0.003032887,0.000137604,0.00008036524,0.0003944758,0.0001808785,0.6913015,0.1020927,0.01408624,0.000611462,0.1873452],"study_design_scores_gemma":[0.00001433656,0.00009611824,0.0009207744,0.000004089743,0.000008860186,0.0001318603,0.0000163968,0.97953,0.01684459,0.002020858,0.0003957383,0.00001629748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06491073,0.00009039481,0.9341292,0.00004052047,0.00001236409,0.00001453389,0.00002215446,0.0001437163,0.0006364083],"genre_scores_gemma":[0.6865315,0.0001702555,0.3110921,0.0001002718,0.00004074265,0.00006202496,0.0001270494,0.00004118224,0.001834778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008856855,"threshold_uncertainty_score":0.004683971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00881672874863595,"score_gpt":0.1932528646109033,"score_spread":0.1844361358622673,"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."}}