{"id":"W3011098329","doi":"10.1016/j.comcom.2020.03.019","title":"Energy-efficient task scheduling and physiological assessment in disaster management using UAV-assisted networks","year":2020,"lang":"en","type":"article","venue":"Computer Communications","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; IBM (Canada); Thompson Rivers University","funders":"","keywords":"Computer science; Scheduling (production processes); Data collection; Energy consumption; Warning system; Emergency management; Internet of Things; Disaster area; Real-time computing; Efficient energy use; Computer security; Telecommunications","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":[0.0002656197,0.0003227797,0.0002367217,0.0002709213,0.0002775471,0.0003165492,0.0002713261,0.0002217568,0.0003791823],"category_scores_gemma":[0.0009879105,0.0001125845,0.0001109922,0.0002548723,0.0001626796,0.0003739336,0.0002887014,0.0001700069,0.00005316727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002738864,"about_ca_system_score_gemma":0.0003910048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004222832,"about_ca_topic_score_gemma":0.005075817,"domain_scores_codex":[0.9998611,0.00004892901,0.000006664216,0.00002670384,0.00002729305,0.00002931118],"domain_scores_gemma":[0.9997308,0.0001463918,0.00003919323,0.00001480512,0.00004816658,0.00002062388],"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":[0.0002720504,0.00007570585,0.003500622,0.0000398701,0.00002400679,0.0000861974,0.00005133009,0.9327419,0.0144444,0.001543141,0.0004556359,0.04676519],"study_design_scores_gemma":[0.000003039527,0.00003787006,0.001028897,0.000001753268,0.000004012115,0.00001816599,0.00002823835,0.9967146,0.001461053,0.0005622609,0.0001375775,0.000002571322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5545014,0.0007882327,0.4408655,0.0002469398,0.0001063795,0.00005041603,0.0000833595,0.0001500083,0.003207763],"genre_scores_gemma":[0.9905803,0.00006212859,0.008934419,0.000007144579,0.000008544584,0.000009662375,0.00001885758,0.00000448217,0.0003745338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004222832,"threshold_uncertainty_score":0.008396506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04870571364179909,"score_gpt":0.2654795059730694,"score_spread":0.2167737923312703,"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."}}