{"id":"W4393121492","doi":"10.5267/j.ijiec.2024.1.002","title":"Truck-drone joint path planning for post-disaster emergency material deployment considering fairness","year":2024,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Software deployment; Truck; Drone; Joint (building); Motion planning; Path (computing); Computer science; Aeronautics; Transport engineering; Engineering; Automotive engineering; Computer network; Robot; Structural engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0004643675,0.0001996283,0.0002592659,0.0005667809,0.00006467591,0.0004367279,0.0006535026,0.00009691587,0.00001926809],"category_scores_gemma":[0.0003199682,0.0001933755,0.0001873378,0.0002173088,0.00001470359,0.0006811095,0.0001332414,0.0003236234,0.000006850564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002014158,"about_ca_system_score_gemma":0.0002512017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007660968,"about_ca_topic_score_gemma":1.060247e-7,"domain_scores_codex":[0.9980701,0.00003285322,0.0008747736,0.0002360139,0.0005352288,0.0002510176],"domain_scores_gemma":[0.9987383,0.0002609621,0.0002391946,0.0001274347,0.0005058199,0.0001282327],"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":[0.00003052221,0.00004845229,0.00008638552,0.00002644154,0.0004132429,0.0003498375,0.001468002,0.9751635,0.003284515,0.004819655,0.002466427,0.011843],"study_design_scores_gemma":[0.001125045,0.0002386524,0.0003703081,0.0009071302,0.00004433326,0.0008987164,0.000111964,0.9907709,0.001514207,0.0009567195,0.002754807,0.0003072595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04810183,0.0001516839,0.9277882,0.001485775,0.02207364,0.0001661738,0.00005083015,0.0001626827,0.00001919196],"genre_scores_gemma":[0.7986374,0.000005910857,0.1982671,0.00004441504,0.002927258,0.00001851148,0.00002766467,0.00003942214,0.00003232853],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7505355,"threshold_uncertainty_score":0.7885623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05161699659116461,"score_gpt":0.2936233720335392,"score_spread":0.2420063754423745,"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."}}