{"id":"W2788137402","doi":"10.1287/ijoc.2021.1066","title":"Network Models for Multiobjective Discrete Optimization","year":2021,"lang":"en","type":"preprint","venue":"INFORMS journal on computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematical optimization; Pareto principle; Multi-objective optimization; Computer science; Shortest path problem; Set (abstract data type); Path (computing); Network planning and design; Optimization problem; Identification (biology); Mathematics; Theoretical computer science","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":[0.001184629,0.001503189,0.0008683409,0.0009520766,0.0005258094,0.001768278,0.001787656,0.001155354,0.005457196],"category_scores_gemma":[0.002788768,0.0005488728,0.001427019,0.001192239,0.001068011,0.002044342,0.001874448,0.003188526,0.0009061205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001961511,"about_ca_system_score_gemma":0.001371516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003664704,"about_ca_topic_score_gemma":0.005084309,"domain_scores_codex":[0.9991428,0.0004080465,0.0000354304,0.0001173059,0.0002397868,0.00005671192],"domain_scores_gemma":[0.9990171,0.0006236215,0.0001054244,0.0001066891,0.0001018873,0.00004524703],"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":[0.00001315124,0.00002275323,0.0001481288,0.00007389142,0.00002451409,0.00003234324,0.00002681205,0.8493627,0.0004515785,0.1371208,0.001048438,0.01167496],"study_design_scores_gemma":[0.000004631393,0.000008362635,0.00002431734,0.00001214489,0.000004437128,0.00001254934,0.000007411059,0.9377868,0.0001424863,0.05890575,0.003086373,0.00000478338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001146632,0.0002367242,0.994998,0.0001996208,0.00003387583,0.00002464188,0.00007053014,0.00007730229,0.003212719],"genre_scores_gemma":[0.2243639,0.002061327,0.7597947,0.0002904734,0.000166503,0.0007336069,0.0005680093,0.000208135,0.01181331],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005457196,"threshold_uncertainty_score":0.01825613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02912103947346487,"score_gpt":0.2954834439798765,"score_spread":0.2663624045064116,"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."}}