{"id":"W7147371040","doi":"10.5937/str2600006s","title":"An active learning framework for drone classification in radio frequency domain","year":2025,"lang":"en","type":"article","venue":"Scientific Technical Review","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja","keywords":"Drone; Active learning (machine learning); Spectrogram; Software deployment; Process (computing); Domain (mathematical analysis); Semi-supervised learning; Frequency domain; Iterative learning control","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":[],"consensus_categories":[],"category_scores_codex":[0.0007252237,0.0000936782,0.0001844268,0.000132881,0.0001223383,0.0000631576,0.0002378549,0.0001071518,0.00003760712],"category_scores_gemma":[0.0002518029,0.00009214412,0.00005274389,0.001322345,0.0000668736,0.0001374782,0.00001319021,0.0002337739,0.00001598694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001560691,"about_ca_system_score_gemma":0.00003725477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001253492,"about_ca_topic_score_gemma":0.0000118995,"domain_scores_codex":[0.9990714,0.00003652242,0.0003173868,0.0003016796,0.0001009921,0.0001720035],"domain_scores_gemma":[0.9993426,0.00009270177,0.00004419078,0.0004161229,0.00006222425,0.00004218502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000005372124,0.0001560258,0.0001941152,0.001835947,0.00001326116,3.903566e-7,0.00007422511,0.00266891,0.033733,0.6314287,0.003487647,0.3264024],"study_design_scores_gemma":[0.0008254955,0.0001162407,0.01513589,0.01363735,0.0001677538,0.000003996704,0.0001879006,0.1008402,0.002490484,0.50941,0.3560994,0.00108534],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001712226,0.01341056,0.9802204,0.0006024291,0.0002099173,0.001233032,0.000008881333,0.0003560167,0.002246529],"genre_scores_gemma":[0.5028447,0.01284736,0.4813184,0.0001504991,0.00005071091,0.002168688,0.0003199736,0.00004220611,0.0002574661],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5011325,"threshold_uncertainty_score":0.3757528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01555297758594906,"score_gpt":0.3170507246972359,"score_spread":0.3014977471112868,"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."}}