{"id":"W3020207422","doi":"10.1109/csci49370.2019.00079","title":"Recognition of Drone Formation Intentions Using Supervised Machine Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Drone; Computer science; Softmax function; Artificial intelligence; Machine learning; Identification (biology); Support vector machine; Plan (archaeology); Computer security; Deep learning; 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":[0.0005945074,0.0005283245,0.0004923985,0.0004388575,0.0002188768,0.0003726052,0.0006292354,0.0004879174,0.0007618687],"category_scores_gemma":[0.002359032,0.0002200381,0.000385392,0.0003016367,0.0002530188,0.000671696,0.0003346765,0.0007268871,0.0003885172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003375248,"about_ca_system_score_gemma":0.0004128117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004495267,"about_ca_topic_score_gemma":0.006804785,"domain_scores_codex":[0.9996073,0.0001123721,0.00002513998,0.0001296672,0.00007082262,0.00005476497],"domain_scores_gemma":[0.9981796,0.0008486385,0.0002397099,0.0002727366,0.0003920001,0.00006752551],"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":[0.0004460761,0.00124572,0.02431891,0.0001141407,0.0001738536,0.00009117398,0.0001364626,0.3679553,0.02380837,0.0008662665,0.002614791,0.5782289],"study_design_scores_gemma":[0.00000576062,0.00006101276,0.003167182,0.000003476652,0.000005552407,0.00001279014,0.00001685077,0.9926905,0.003500943,0.0003862465,0.0001441558,0.000005518673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5735654,0.0003612227,0.4215519,0.0002259049,0.00008280516,0.0001164267,0.000324371,0.001741499,0.002030536],"genre_scores_gemma":[0.9451645,0.000059103,0.05285774,0.00003771598,0.00002465693,0.0000456346,0.0006717782,0.00002237229,0.001116441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004495267,"threshold_uncertainty_score":0.008938193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06305058343588359,"score_gpt":0.2937886414169703,"score_spread":0.2307380579810867,"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."}}