{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002463231,0.0003355868,0.0002923726,0.00009506468,0.0004632344,0.0001746418,0.001033753,0.0001737217,0.0000385832],"category_scores_gemma":[0.00002278787,0.0003326285,0.00008996739,0.0005983051,0.00005294248,0.00009161282,0.004807525,0.001259699,0.00001612128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001928478,"about_ca_system_score_gemma":0.00005620944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001958348,"about_ca_topic_score_gemma":0.00005815152,"domain_scores_codex":[0.9975907,0.0001294291,0.0002811123,0.001214571,0.0003614058,0.000422839],"domain_scores_gemma":[0.9979076,0.0002195759,0.0002428605,0.001408646,0.0000626007,0.000158668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000121639,0.00005359972,0.00003931166,0.00003099713,0.00001522555,0.000003646495,0.00021248,0.8415323,0.0001861044,0.005892539,0.001557582,0.150464],"study_design_scores_gemma":[0.0002491839,0.00004917521,0.002292618,0.00004320675,0.00002195041,0.00002348881,0.00001249421,0.9108812,0.0003498405,0.009742137,0.07574791,0.0005868475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006891816,0.002814303,0.9848567,0.001083261,0.001502758,0.001179737,0.000007519674,0.0006759164,0.0009879353],"genre_scores_gemma":[0.6684085,0.001074981,0.3239258,0.0004738175,0.001042503,0.001415219,0.00004794537,0.00008579503,0.003525374],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6615167,"threshold_uncertainty_score":0.9999126,"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."}}