{"id":"W2954664346","doi":"10.22158/asir.v3n3p92","title":"A Stochastic Simulation-Optimization Method for Generating Waste Management Alternatives Using Population-Based Algorithms","year":2019,"lang":"en","type":"article","venue":"Applied Science and Innovative Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Mathematical optimization; Computer science; Process (computing); Population; Stochastic optimization; Stochastic simulation; Stochastic process; Algorithm; Mathematics","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":[0.003702312,0.0001940074,0.0002153642,0.001204036,0.001050224,0.0004583175,0.0006879204,0.00005085286,0.000009463688],"category_scores_gemma":[0.000374432,0.0001848687,0.00002063117,0.006538521,0.00025535,0.00118079,0.0004114041,0.0002079461,0.00000576132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003981095,"about_ca_system_score_gemma":0.0003158932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000022147,"about_ca_topic_score_gemma":6.329616e-7,"domain_scores_codex":[0.9965552,0.0001083719,0.0003682594,0.001018788,0.001342923,0.000606465],"domain_scores_gemma":[0.9953436,0.000798745,0.0001941617,0.0004283525,0.003133224,0.0001019714],"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":[0.00001231458,0.00003190667,0.00005232772,0.00002256652,0.00001016689,3.888861e-7,0.0002951288,0.9238666,0.005710429,0.03747923,0.000001657749,0.03251732],"study_design_scores_gemma":[0.0008168349,0.00005944922,0.000137607,0.00003022051,0.000001855535,7.561169e-7,0.0004406756,0.9940621,0.00260836,0.001610687,0.00001237014,0.0002190755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004185122,0.00001200608,0.9931039,0.00008978933,0.0001269017,0.00188052,0.000005447736,0.00006166304,0.0005347159],"genre_scores_gemma":[0.3180498,0.00000120093,0.6815819,0.0001249317,0.00004233855,0.0001228456,0.000009287302,0.00001437922,0.00005331259],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3138647,"threshold_uncertainty_score":0.8077579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06961703694971053,"score_gpt":0.4230346416882911,"score_spread":0.3534176047385805,"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."}}