{"id":"W2073224088","doi":"10.1139/l06-087","title":"Dynamic programming with the principle of progressive optimality for searching rule curves","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water resources management and optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Dynamic programming; Curse of dimensionality; Reservoir computing; Convergence (economics); Computer science; Nonlinear system; Key (lock); Mathematics; Nonlinear programming; Artificial intelligence; Artificial neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003071701,0.001090036,0.001325943,0.0009354616,0.0005267057,0.001329189,0.001258012,0.00123248,0.002003316],"category_scores_gemma":[0.008115718,0.0008459614,0.001160422,0.001289649,0.001761503,0.001996679,0.001460465,0.002590139,0.0003093265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009544403,"about_ca_system_score_gemma":0.001871132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002398439,"about_ca_topic_score_gemma":0.00176287,"domain_scores_codex":[0.9989856,0.0003939032,0.00005278118,0.0001993313,0.0002932331,0.00007522479],"domain_scores_gemma":[0.9977639,0.001737289,0.0001567318,0.00008097106,0.0002198035,0.00004134281],"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":[0.00002178533,0.00002754906,0.000283024,0.0001025482,0.00003032258,0.00004469608,0.00006563703,0.8659895,0.000731351,0.09771924,0.0004899608,0.03449436],"study_design_scores_gemma":[0.000009528426,0.00003303468,0.0000565575,0.00001217876,0.000005372457,0.00001733789,0.000006874151,0.9649251,0.0003189274,0.0337126,0.0008934524,0.000009042625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002577643,0.0001301587,0.9955919,0.00006442863,0.000009535761,0.00003673467,0.00001269231,0.00002715307,0.001549623],"genre_scores_gemma":[0.262946,0.0009056918,0.7313036,0.00009686368,0.00007815317,0.0006696712,0.000114526,0.0001086763,0.00377683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003071701,"threshold_uncertainty_score":0.01624489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006160011396714219,"score_gpt":0.2089687264793297,"score_spread":0.2028087150826155,"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."}}