{"id":"W2902619956","doi":"10.4018/ijsds.2019010103","title":"Modelling the Deterioration of Bridge Decks Based on Semi-Markov Decision Process","year":2018,"lang":"en","type":"article","venue":"International Journal of Strategic Decision Sciences","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Weibull distribution; Latin hypercube sampling; Markov chain; Goodness of fit; Statistics; Statistic; Mathematics; Computer science; Mean squared error; Markov model; Monte Carlo method","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":[0.0009526277,0.0001142714,0.000144688,0.0003000698,0.0001088525,0.0001735948,0.0008181205,0.00004877136,0.00004601811],"category_scores_gemma":[0.0001091323,0.00006707129,0.00008403454,0.000296967,0.0001885536,0.0003754701,0.00002028283,0.0001582369,0.000006165309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005629536,"about_ca_system_score_gemma":0.000131756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006503755,"about_ca_topic_score_gemma":0.000003684793,"domain_scores_codex":[0.9979147,0.00001550643,0.0005619965,0.0001217695,0.001241989,0.0001440451],"domain_scores_gemma":[0.9983314,0.0003127915,0.000264494,0.0001076774,0.0009362741,0.00004731227],"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":[0.0001512276,0.00001410211,0.0002724706,0.000003074319,0.00001400387,0.000009644798,0.0001805135,0.9732529,0.001884944,0.0004316623,0.0001538154,0.02363167],"study_design_scores_gemma":[0.0003951264,0.0002629653,0.0007801311,0.0005075959,0.000008563761,0.00006145759,0.0003262237,0.9467758,0.01171313,0.03887193,0.0001893773,0.0001076899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6973726,0.00003530736,0.2975543,0.00004335377,0.002465702,0.00003799075,0.000002579266,0.000008127466,0.002480066],"genre_scores_gemma":[0.9901364,0.00003179072,0.009227511,0.00004793548,0.0005449079,0.000001041394,3.656905e-7,0.000007312757,0.00000274562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2927638,"threshold_uncertainty_score":0.2735088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04951495869825659,"score_gpt":0.3319589319240851,"score_spread":0.2824439732258285,"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."}}