{"id":"W2043793098","doi":"10.4236/jwarp.2012.45030","title":"Adaptive Neuro-Fuzzy Logic System for Heavy Metal Sorption in Aquatic Environments","year":2012,"lang":"en","type":"article","venue":"Journal of Water Resource and Protection","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adaptive neuro fuzzy inference system; Mercury (programming language); Fuzzy logic; Inference; Artificial neural network; Environmental science; Sorption; Neuro-fuzzy; Computer science; Data mining; Adsorption; Machine learning; Artificial intelligence; Fuzzy control system; Chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009333572,0.0001000026,0.0001536451,0.00009190107,0.00007815596,0.00002006075,0.00009228615,0.00008417363,0.000005412723],"category_scores_gemma":[0.00003210523,0.00006615784,0.0000497059,0.000056008,0.00005959235,0.0003517552,0.00007154202,0.0001846252,0.00002227341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002173897,"about_ca_system_score_gemma":0.00000107355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009161309,"about_ca_topic_score_gemma":0.000002521964,"domain_scores_codex":[0.9990256,0.0001324397,0.0002984848,0.0001105018,0.0002015321,0.000231405],"domain_scores_gemma":[0.9996783,0.00002219335,0.0001591727,0.00008943315,0.000001880442,0.00004906563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001454966,0.0004403323,0.03957436,0.0001511082,0.00008167119,0.00001678079,0.006698786,0.003186228,0.9086177,0.00007188701,0.0001427095,0.0395635],"study_design_scores_gemma":[0.002052755,0.002631463,0.03875067,0.0001892869,0.0001165807,0.0005180934,0.003295452,0.001418316,0.9237371,0.005443131,0.02138366,0.0004634717],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925678,0.00006187958,0.006455178,0.0003372758,0.0001396968,0.0003683271,5.58345e-7,0.00001881255,0.00005047194],"genre_scores_gemma":[0.9987888,0.00000505498,0.0009623598,0.00001640271,0.0001183253,0.00003157979,4.714748e-7,0.000009582875,0.00006745964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03910003,"threshold_uncertainty_score":0.2697838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04615771196403861,"score_gpt":0.2413519723074531,"score_spread":0.1951942603434144,"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."}}