{"id":"W2893895554","doi":"10.1287/ijoc.2017.0795","title":"Risk Averse Shortest Paths: A Computational Study","year":2018,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Shortest path problem; Measure (data warehouse); Mathematical optimization; Risk measure; Variance (accounting); Mathematics; Path (computing); Regular polygon; Implementation; Arc length; Computer science; Arc (geometry); Discrete mathematics; Data mining","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005049989,0.0001744833,0.0002643659,0.0005262489,0.001090444,0.000925126,0.0005940347,0.00005805996,0.0002756962],"category_scores_gemma":[0.001752776,0.000113546,0.0001354221,0.0008504654,0.00009037745,0.0006450283,0.0001264434,0.0004482397,0.001131694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006208316,"about_ca_system_score_gemma":0.0001444525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001463735,"about_ca_topic_score_gemma":0.0000108968,"domain_scores_codex":[0.9962236,0.0001641864,0.001204629,0.0002611737,0.001836791,0.0003096619],"domain_scores_gemma":[0.9964309,0.0009262742,0.001022829,0.0002902414,0.001114991,0.0002147726],"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":[0.00006006282,0.0001546833,0.2543443,1.783571e-7,0.00003346779,0.00004130746,0.00383704,0.404368,4.266183e-7,0.000212503,0.004391072,0.3325569],"study_design_scores_gemma":[0.001248219,0.001545036,0.270146,0.00002838418,0.00002027398,0.0002641749,0.003494325,0.6970125,0.000006160261,0.009608698,0.01636091,0.0002653499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8335867,0.000007752144,0.1569486,0.00007100275,0.001072667,0.0001625892,0.000004216552,0.00004215604,0.008104298],"genre_scores_gemma":[0.9916256,0.00001278796,0.006754437,0.0004147043,0.00100525,5.299928e-7,0.000002222262,0.00001124775,0.0001732111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3322916,"threshold_uncertainty_score":0.9996461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06202038414082085,"score_gpt":0.3816068146451262,"score_spread":0.3195864305043054,"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."}}