{"id":"W4402125019","doi":"10.1109/tmm.2024.3453044","title":"Coarse-to-Fine Target Detection for HFSWR With Spatial-Frequency Analysis and Subnet Structure","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Nuclear Physics and Applications","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Subnet; Computer science; Telecommunications; Computer network","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":[0.00003534506,0.0001637051,0.0001731618,0.0001915659,0.0001759493,0.00008367219,0.00006641827,0.00003840201,0.0002354736],"category_scores_gemma":[4.175971e-7,0.0001398135,0.000115212,0.0005765151,0.0000378853,0.00008842651,7.842542e-7,0.0001644101,0.00002878085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002081183,"about_ca_system_score_gemma":0.00002729552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005043749,"about_ca_topic_score_gemma":0.0005337648,"domain_scores_codex":[0.9992297,0.00001208742,0.0001380512,0.0003394614,0.0001063014,0.0001744448],"domain_scores_gemma":[0.9995233,0.00008035823,0.00002849112,0.000199893,0.00005414031,0.0001137999],"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":[0.0001169003,0.000307309,0.0006104014,0.00006049461,0.002228187,0.000001576269,0.00104412,0.02461394,0.1237306,0.001304803,0.0001618173,0.8458199],"study_design_scores_gemma":[0.001485311,0.000568127,0.003565281,0.00006418591,0.002575102,0.000002381402,0.0002182294,0.560994,0.4145525,0.009297309,0.005720824,0.0009567398],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1199026,0.000008144985,0.8782009,0.000170235,0.0001483799,0.0003950845,0.001003294,0.0000943543,0.00007700122],"genre_scores_gemma":[0.9848749,0.000001131098,0.01449838,0.00002277245,0.0001857485,0.0001729722,0.00006055145,0.00004323165,0.0001403004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8649723,"threshold_uncertainty_score":0.5701428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005298795199876363,"score_gpt":0.2313363966028328,"score_spread":0.2260376014029565,"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."}}