{"id":"W4311622297","doi":"10.36227/techrxiv.21624585","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":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"","keywords":"Drone; Computer science; Focus (optics); Scalability; Feature (linguistics); Convolution (computer science); Backbone network; Popularity; Deep learning; Object detection; Artificial intelligence; Architecture; Distributed computing; Real-time computing; Pattern recognition (psychology); Computer network; Artificial neural network; Database; Geography","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":[0.0003901688,0.0009888752,0.0005584349,0.001121401,0.0004660471,0.0006640146,0.001755062,0.000845266,0.003163091],"category_scores_gemma":[0.001254556,0.0003523848,0.0004376983,0.0005177973,0.0004365487,0.001718153,0.001353504,0.0008850465,0.0009628471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001353337,"about_ca_system_score_gemma":0.0009646069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01578904,"about_ca_topic_score_gemma":0.02646186,"domain_scores_codex":[0.9997438,0.00002449514,0.00001274983,0.00009131618,0.00007740596,0.00005030023],"domain_scores_gemma":[0.9997832,0.00004680435,0.0000281484,0.00004622903,0.00006980895,0.0000258303],"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":[0.0006705435,0.000259421,0.004000977,0.0002298404,0.0001022763,0.0002877225,0.0001850582,0.1599115,0.0239307,0.01367703,0.05866395,0.7380811],"study_design_scores_gemma":[0.00002650043,0.0001183178,0.0008886185,0.00002092386,0.00001542132,0.00007857384,0.0000293962,0.9782313,0.008224168,0.003648015,0.008696768,0.0000220041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1612661,0.002929743,0.7895475,0.001179855,0.0005917219,0.0003908429,0.003785592,0.02729555,0.01301317],"genre_scores_gemma":[0.6800972,0.0009045565,0.2954183,0.0007600099,0.000123658,0.0003139595,0.009005841,0.000370868,0.01300569],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01578904,"threshold_uncertainty_score":0.03139424,"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."}}