{"id":"W2794631670","doi":"10.1016/j.engappai.2018.03.010","title":"A Monte-Carlo-based interval De Novo programming method for optimal system design under uncertainty","year":2018,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Water resources management and optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"National Key Research and Development Program of China; Beijing Normal University","keywords":"Computer science; Monte Carlo method; Interval (graph theory); Flexibility (engineering); Mathematical optimization; Electricity; Linear programming; Path (computing); Operations research; Reliability engineering; Algorithm; Mathematics; Statistics","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":[0.002713572,0.0009145061,0.001815467,0.0009491475,0.0006355945,0.001047532,0.00160655,0.001810304,0.003892345],"category_scores_gemma":[0.005184177,0.001099136,0.001119037,0.0008704346,0.0008536177,0.001041263,0.001259171,0.001903044,0.0005129343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008232508,"about_ca_system_score_gemma":0.001690489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004957473,"about_ca_topic_score_gemma":0.004605257,"domain_scores_codex":[0.9993698,0.0002859003,0.00002620557,0.00007642581,0.0001935395,0.00004807592],"domain_scores_gemma":[0.9971051,0.00223433,0.0001499532,0.00009298188,0.0003311078,0.00008649459],"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":[0.00002871271,0.00002764173,0.00009846245,0.00003380453,0.00002591117,0.00002244331,0.00001912626,0.9775065,0.0004552419,0.006610986,0.0002244341,0.01494663],"study_design_scores_gemma":[0.000004411924,0.000006152691,0.00001409703,0.0000035825,0.000002828145,0.000003416187,8.886111e-7,0.9989249,0.00006142042,0.0008440425,0.00013181,0.000002490026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002860142,0.0001111853,0.9951373,0.00005648477,0.00002707039,0.00002612217,0.00001676382,0.0001241627,0.001640858],"genre_scores_gemma":[0.2355841,0.0002406267,0.7606854,0.0001457785,0.00007847527,0.0003711457,0.0001158816,0.000206973,0.002571556],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004957473,"threshold_uncertainty_score":0.01435095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03213069268111453,"score_gpt":0.2764309690686004,"score_spread":0.2443002763874859,"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."}}