{"id":"W2017677001","doi":"10.1002/atr.173","title":"Solving the logistic problems with optimal resource assignment using fuzzy logic methods","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scope (computer science); Resource (disambiguation); Fuzzy logic; Computer science; Operations research; Basis (linear algebra); Mathematical optimization; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002045173,0.0009397061,0.001502015,0.001382634,0.0007085979,0.001775339,0.001165842,0.001544687,0.002690944],"category_scores_gemma":[0.002978412,0.0006483081,0.001294258,0.001358349,0.0007581941,0.00098623,0.001075575,0.001018554,0.0002620574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001300876,"about_ca_system_score_gemma":0.002494077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007185877,"about_ca_topic_score_gemma":0.005109408,"domain_scores_codex":[0.9990324,0.0004704797,0.0000566085,0.00009892254,0.0002243591,0.0001172645],"domain_scores_gemma":[0.9990931,0.0006754755,0.00007959722,0.00002260086,0.00009966984,0.00002957947],"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":[0.00003285479,0.00003248837,0.0001490156,0.00007745306,0.00002642354,0.00005648673,0.00005330804,0.9700608,0.0004609475,0.007926032,0.0002471803,0.02087702],"study_design_scores_gemma":[0.00001704127,0.00002169287,0.000041489,0.00001057143,0.000008852427,0.00001372632,0.00002637544,0.9889401,0.0002624495,0.01037968,0.0002716823,0.000006366582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01336225,0.0002552771,0.983937,0.0001431342,0.00001400076,0.00006607301,0.00003222974,0.00005672183,0.00213324],"genre_scores_gemma":[0.4115362,0.0005678968,0.5848911,0.00007212707,0.00006678467,0.0004047758,0.0001031363,0.00003292004,0.00232505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007185877,"threshold_uncertainty_score":0.01428813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.318050227655786,"score_gpt":0.4530191915363747,"score_spread":0.1349689638805887,"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."}}