{"id":"W2172724430","doi":"10.5194/hess-20-2267-2016","title":"Dissolved oxygen prediction using a possibility theory based fuzzy neural network","year":2016,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Università degli Studi di Ferrara; Ministry of Advanced Education; University of Victoria","keywords":"Fuzzy logic; Artificial neural network; Defuzzification; Computer science; Construct (python library); Neuro-fuzzy; Data mining; Artificial intelligence; Fuzzy set; Machine learning; Fuzzy control system; Fuzzy number","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004832535,0.0002982137,0.0003255907,0.0006156074,0.0003199067,0.000654458,0.0005626981,0.0004723105,0.0008426841],"category_scores_gemma":[0.00135411,0.0001916426,0.0003545152,0.0003928751,0.0003481816,0.0005158964,0.0002872747,0.0003844795,0.00009880737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001356383,"about_ca_system_score_gemma":0.0007174531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03472231,"about_ca_topic_score_gemma":0.02675489,"domain_scores_codex":[0.9998205,0.00004432958,0.00001101666,0.0000391545,0.00006753385,0.00001742563],"domain_scores_gemma":[0.9995227,0.000249789,0.00004044479,0.0000127373,0.0001538977,0.00002041823],"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":[0.0001058536,0.00004284923,0.003734051,0.00003978003,0.00004579479,0.00006744757,0.0000362288,0.9475347,0.002283365,0.00192351,0.0004315502,0.04375484],"study_design_scores_gemma":[0.000004199611,0.000007990725,0.0002560904,0.000001797011,0.000002902033,0.000002900268,0.000002064628,0.9991624,0.0001837315,0.0003193787,0.00005436056,0.000002175339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3223644,0.0004334981,0.6713815,0.000432441,0.00009405555,0.00006111691,0.0001718198,0.0003754011,0.004685717],"genre_scores_gemma":[0.9521737,0.00007861363,0.0464063,0.00002695238,0.00001604935,0.00002872227,0.00006293566,0.000004990383,0.001201839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03472231,"threshold_uncertainty_score":0.06904036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540748086991793,"score_gpt":0.2329276834398698,"score_spread":0.2075202025699519,"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."}}