{"id":"W2743141345","doi":"10.1109/icuas.2017.7991527","title":"Multiple UAVs in forest fire fighting mission using particle swarm optimization","year":2017,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Firefighting; Particle swarm optimization; Task (project management); Computer science; Position (finance); Fire control; Motion planning; Path (computing); Swarm behaviour; Simulation; Real-time computing; Artificial intelligence; Engineering; Robot; Algorithm; Geography; Systems engineering","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":[0.0001045321,0.00005671123,0.00005458206,0.00000694224,0.0004353168,0.0000725412,0.0001240797,0.00003509406,0.0001809989],"category_scores_gemma":[0.00009239173,0.00004924469,0.00001799205,0.00005172552,0.00005590793,0.000196902,0.00009209206,0.00004685267,0.00008079875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006000693,"about_ca_system_score_gemma":0.000003934698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003313957,"about_ca_topic_score_gemma":0.0006996458,"domain_scores_codex":[0.9994617,0.00001362514,0.0001142393,0.0001688296,0.00009454745,0.0001471178],"domain_scores_gemma":[0.9995512,0.00002244567,0.00006484984,0.0003054709,0.00000356618,0.00005245512],"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":[0.000003448572,0.00003640332,0.2405127,0.000001398524,7.18132e-7,0.000001530676,0.0001544136,0.7363853,0.01173648,0.0000141063,0.0001033932,0.01105011],"study_design_scores_gemma":[0.0001635138,0.00000431374,0.09807657,0.00001160779,0.00000185984,0.0000016993,0.00004858534,0.8955488,0.005551507,0.0000514474,0.0004746644,0.00006546552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751059,0.000003167305,0.0151093,0.000320657,0.0000290867,0.0001036537,3.817858e-7,0.00002713273,0.009300705],"genre_scores_gemma":[0.9677254,0.000002880737,0.03168793,0.00003293678,0.00001818517,4.75094e-7,0.000002331763,0.000007052737,0.0005228331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1591634,"threshold_uncertainty_score":0.5009733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03112608444054286,"score_gpt":0.2748018243131431,"score_spread":0.2436757398726002,"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."}}