{"id":"W4243627243","doi":"10.5194/hessd-12-12311-2015","title":"Dissolved oxygen prediction using a possibility-theory based fuzzy neural network","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fuzzy logic; Artificial neural network; Defuzzification; Construct (python library); Computer science; Resource (disambiguation); Data mining; Neuro-fuzzy; Fuzzy set; Artificial intelligence; Machine learning; Fuzzy control system; Fuzzy number","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002295555,0.0004343026,0.0004251311,0.00003726579,0.0002360672,0.0001180241,0.0005751256,0.0005373828,0.002150246],"category_scores_gemma":[0.0003509774,0.000357315,0.0002037826,0.0002618954,0.0004169246,0.0001188415,0.001726534,0.0008403445,0.000192701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007884437,"about_ca_system_score_gemma":0.00007079269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007006024,"about_ca_topic_score_gemma":0.0000830093,"domain_scores_codex":[0.9964994,0.0005738483,0.0005336212,0.001104101,0.0006029001,0.0006861953],"domain_scores_gemma":[0.9983299,0.000141666,0.0002559467,0.0008999057,0.0000260619,0.0003464952],"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":[0.00009717426,0.00008492353,0.02949367,0.00001746567,0.00001515157,0.00001012577,0.00007356191,0.9669448,0.0003117937,0.00002614853,0.001679274,0.001245906],"study_design_scores_gemma":[0.0002484919,0.00009810911,0.01887832,0.00006247377,0.00007664997,0.000007772766,0.000005230693,0.9377806,0.00002783616,0.04208456,0.0003430086,0.0003869031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732894,0.00003400625,0.01006352,0.0001734774,0.001116906,0.0005929512,0.00003911588,0.0005027861,0.01418784],"genre_scores_gemma":[0.9783073,7.005406e-7,0.01983611,0.0005838548,0.0004016699,0.00002352999,0.0001005982,0.00004684882,0.0006993596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04205842,"threshold_uncertainty_score":0.9998879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0522504857865821,"score_gpt":0.2735198181119239,"score_spread":0.2212693323253418,"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."}}