{"id":"W4311622298","doi":"10.36227/techrxiv.21624585.v1","title":"SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"","keywords":"Drone; Computer science; Focus (optics); Scalability; Feature (linguistics); Backbone network; Convolution (computer science); Popularity; Deep learning; Object detection; Artificial intelligence; Distributed computing; Real-time computing; Pattern recognition (psychology); Computer network; Artificial neural network; Database","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":[0.0003990023,0.0009782672,0.0005670183,0.001146686,0.0004712965,0.0006597778,0.001720714,0.0008328735,0.00276975],"category_scores_gemma":[0.001348541,0.0003304585,0.0004396644,0.0005161453,0.0004317011,0.001780609,0.001424651,0.0008625243,0.0009009015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001252247,"about_ca_system_score_gemma":0.0009102221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01329521,"about_ca_topic_score_gemma":0.02169249,"domain_scores_codex":[0.9997465,0.00002414859,0.00001250971,0.00008772859,0.00007845378,0.0000506383],"domain_scores_gemma":[0.9997746,0.00004917785,0.0000315116,0.00004599262,0.00007187493,0.00002688035],"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":[0.0006924086,0.0002623226,0.004976511,0.000248278,0.0001039647,0.0003159038,0.0002238199,0.1532048,0.02317942,0.01311951,0.05500811,0.7486649],"study_design_scores_gemma":[0.00003029402,0.0001431358,0.001108126,0.00002819865,0.00001887378,0.0001060197,0.0000380994,0.9748715,0.009594494,0.004521789,0.009513325,0.00002617158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1732618,0.002963508,0.7817957,0.001162359,0.0005717304,0.0003827049,0.003581619,0.02462012,0.0116605],"genre_scores_gemma":[0.6986958,0.0008843222,0.279101,0.0007439777,0.0001188498,0.0003011231,0.008481337,0.0003602384,0.01131338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01329521,"threshold_uncertainty_score":0.02643561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01141655384530416,"score_gpt":0.2548248554988958,"score_spread":0.2434083016535917,"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."}}