{"id":"W4387846642","doi":"10.1145/3583780.3615306","title":"Anomaly and Novelty detection for Satellite and Drone systems (ANSD '23)","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Institute for Information and Communications Technology Promotion; University of California, San Diego; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Korea Aerospace Research Institute; National University of Computer and Emerging Sciences; National Research Foundation; Sungkyunkwan University; Commonwealth Scientific and Industrial Research Organisation; Sangmyung University; University of Southern California; Kyungpook National University; Stony Brook University; State University of New York; Chungnam National University; Institute for Catastrophic Loss Reduction; University of Washington; National Aeronautics and Space Administration","keywords":"Drone; Anomaly detection; Computer science; Novelty; Satellite; Novelty detection; Scale (ratio); Data science; Anomaly (physics); Remote sensing; Real-time computing; Data mining; Geography; Cartography; Engineering; Aerospace engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001790716,0.00007198692,0.00008378819,0.00009225109,0.000179025,0.0001477877,0.000106369,0.00005055071,8.218042e-7],"category_scores_gemma":[0.000006441367,0.00006508546,0.00002003387,0.0003272043,0.00002607677,0.0001897203,0.00007986953,0.00003538323,0.00001354452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001223301,"about_ca_system_score_gemma":0.000006815664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006656,"about_ca_topic_score_gemma":0.00002475832,"domain_scores_codex":[0.9993951,0.000009323795,0.000127749,0.0002718989,0.00006152676,0.0001344448],"domain_scores_gemma":[0.9996124,0.00005649279,0.00003869475,0.0001967724,0.00004041044,0.00005526466],"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":[0.00001451719,0.00004566857,0.001803427,0.0001763138,0.00003567912,0.000001846222,0.0003581629,0.00003866135,0.07950746,0.2993897,0.00134783,0.6172807],"study_design_scores_gemma":[0.0007155356,0.0005515006,0.05664244,0.0000295745,0.00002197151,0.0001188238,0.0002404624,0.6698929,0.06596087,0.01153689,0.193688,0.0006009722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06002605,0.0001304504,0.9376853,0.0003418981,0.00007472696,0.0003714827,0.000003473685,0.0007189529,0.0006477322],"genre_scores_gemma":[0.9848636,0.0001837704,0.01222628,0.00005388595,0.00003918023,0.0002124975,0.000002035859,0.000006923696,0.002411782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.925459,"threshold_uncertainty_score":0.2654108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01691208634864753,"score_gpt":0.2467765818839601,"score_spread":0.2298644955353126,"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."}}