{"id":"W4231016262","doi":"10.1109/wsc.2002.1166469","title":"Selecting earthmoving equipment fleets using genetic algorithms","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Concordia University; Cairo University; National Science Council","keywords":"Genetic algorithm; Computer science; Algorithm; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00005955328,0.0001073236,0.00008267188,0.00005060332,0.00008480941,0.00002983638,0.00003720864,0.00003983437,0.0000774259],"category_scores_gemma":[0.00003401832,0.0001116297,0.00001979558,0.00008947725,0.000008481561,0.00006270513,0.000009007,0.00008182576,0.00001182129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006776534,"about_ca_system_score_gemma":0.000008788932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006881535,"about_ca_topic_score_gemma":0.000001837234,"domain_scores_codex":[0.999411,0.00001142241,0.0001365018,0.0001216479,0.00008156132,0.0002378449],"domain_scores_gemma":[0.9997896,0.00002409004,0.00001583992,0.0001022525,0.00001924106,0.00004895638],"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":[2.391494e-7,0.000003620852,0.00005359147,0.00001195029,0.000007900353,0.000002889364,0.00004149348,0.994571,0.001048206,0.0002250984,0.00001196039,0.00402206],"study_design_scores_gemma":[0.0001342326,0.00001027856,0.0001041398,0.00001146806,0.00000907917,0.00002143173,0.00004448641,0.9379702,0.06007897,0.0005409786,0.0008747466,0.0001999715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03316574,0.0001457633,0.9561582,0.000001612732,0.0002618255,0.00006600759,3.905833e-7,0.0003548972,0.009845609],"genre_scores_gemma":[0.5916862,0.00001725397,0.4081214,0.00001158415,0.0000302626,0.000001965916,9.403466e-7,0.00002071595,0.0001095711],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5585205,"threshold_uncertainty_score":0.4552129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01923168373033802,"score_gpt":0.2352435042204465,"score_spread":0.2160118204901085,"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."}}