{"id":"W1967455052","doi":"10.1007/s11269-005-3275-3","title":"Reservoir Operation Using a Dynamic Programming Fuzzy Rule–Based Approach","year":2005,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Mathematical optimization; Fuzzy logic; Set (abstract data type); Dynamic programming; Fuzzy rule; Function (biology); Variance (accounting); Fuzzy set; Algorithm; Mathematics; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003012325,0.0003230306,0.0002077054,0.0004091168,0.0002184218,0.0004206278,0.000378555,0.00007826766,0.00006110196],"category_scores_gemma":[0.000001207523,0.000265825,0.00009214921,0.0002310009,0.00003774057,0.0003542494,0.0001880427,0.0001402759,0.0001216581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002219205,"about_ca_system_score_gemma":0.000001292723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001384939,"about_ca_topic_score_gemma":0.00001031527,"domain_scores_codex":[0.9981576,0.00005290722,0.0003864755,0.0004064201,0.0003935406,0.000603108],"domain_scores_gemma":[0.9994025,0.000004194502,0.00003872923,0.0004434739,0.0000230144,0.00008807004],"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":[0.00001919201,0.0000687704,0.0001090352,0.0003255418,0.00009593592,0.000008445983,0.001221213,0.9834234,0.0003927606,0.00004450672,0.0001490575,0.01414214],"study_design_scores_gemma":[0.0005078999,0.00001848667,0.0001017127,0.00003904488,0.00007515468,0.000001316511,0.0002002359,0.824406,0.0009699713,0.00002470946,0.1733146,0.0003408117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7342861,0.0002293917,0.2204545,0.0002811976,0.0001659275,0.001560277,0.00000322544,0.001393161,0.04162619],"genre_scores_gemma":[0.9045499,0.00001628308,0.09251539,0.0001092609,0.0001524295,0.0001360706,0.0001988749,0.0001029961,0.002218785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1731656,"threshold_uncertainty_score":0.9999794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01103201007142037,"score_gpt":0.2062367619478763,"score_spread":0.1952047518764559,"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."}}