{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001371044,0.0001449315,0.0001949666,0.00007108976,0.0000496791,0.0000135065,0.00006714298,0.00007769219,0.00009385522],"category_scores_gemma":[0.00002420075,0.0001405243,0.00005319707,0.0001224616,0.00008133164,0.0001647165,0.000009029131,0.000134965,0.00002190958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005234076,"about_ca_system_score_gemma":0.00001927674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002023778,"about_ca_topic_score_gemma":0.00001421781,"domain_scores_codex":[0.9991025,0.00003420451,0.0002364945,0.0001068811,0.0001359227,0.0003839709],"domain_scores_gemma":[0.9996255,0.00009461476,0.00004975899,0.0001108424,0.00003579442,0.0000834762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000126575,0.00002705453,0.0001103324,0.00007078242,0.00005426594,0.000001102517,0.0004389273,0.0003833756,0.9887159,0.000228237,0.0001435265,0.009813805],"study_design_scores_gemma":[0.001301037,0.0008669816,0.01417729,0.000128056,0.00007614193,0.000176004,0.001119097,0.05157242,0.9276656,0.001059336,0.001175032,0.0006830215],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9478992,0.01290148,0.03217639,0.00001664261,0.0005925343,0.0002452162,0.000006326737,0.000207285,0.005954918],"genre_scores_gemma":[0.9987215,0.00002148718,0.0009426597,0.0000169086,0.0001146458,0.000008034322,0.000002116399,0.0000294548,0.0001432384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06105035,"threshold_uncertainty_score":0.5730415,"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."}}