{"id":"W4403080156","doi":"10.5391/ijfis.2024.24.3.194","title":"Features Exploitation of YOLOv5-Based Freeze Backbone for Performance Improvement of UAV Object Detection","year":2024,"lang":"en","type":"article","venue":"International Journal of Fuzzy Logic and Intelligent Systems","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Universitas Telkom","keywords":"Object detection; Artificial intelligence; Computer science; Object (grammar); Computer vision; Pattern recognition (psychology)","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":[0.0001760589,0.0003409208,0.0003495714,0.0004510751,0.0002715018,0.0003545012,0.0006378097,0.0002312097,0.002775809],"category_scores_gemma":[0.0003420858,0.0001033736,0.0002055177,0.0002664111,0.0001419551,0.0004712314,0.0004303881,0.0002446978,0.0006714294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002686167,"about_ca_system_score_gemma":0.000323075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002179676,"about_ca_topic_score_gemma":0.003789228,"domain_scores_codex":[0.999908,0.000008636822,0.000003361468,0.00002073554,0.0000381727,0.00002114802],"domain_scores_gemma":[0.9999102,0.00001313722,0.00001020018,0.00001246172,0.00004082178,0.00001319204],"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":[0.001405824,0.0003342591,0.006343229,0.000154239,0.00008273886,0.0002938088,0.0001594576,0.05918537,0.2401607,0.003389569,0.009652153,0.6788387],"study_design_scores_gemma":[0.00006213567,0.0006190692,0.005350928,0.00002222469,0.00005641806,0.0001929649,0.0001014113,0.9074826,0.07734317,0.001344947,0.007391897,0.00003222443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4824107,0.002370028,0.4918443,0.0003604274,0.0004726971,0.0001399872,0.0003605377,0.004157113,0.01788422],"genre_scores_gemma":[0.9269482,0.0003268198,0.06790075,0.0001026835,0.00003496139,0.0000428017,0.0004656969,0.00007075804,0.004107302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002775809,"threshold_uncertainty_score":0.009286046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01572773192980015,"score_gpt":0.2626899114480054,"score_spread":0.2469621795182052,"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."}}