{"id":"W2055366723","doi":"10.1039/c2em30372h","title":"Coupling geostatistical approaches with PCA and fuzzy optimal model (FOM) for the integrated assessment of sampling locations of water quality monitoring networks (WQMNs)","year":2012,"lang":"en","type":"article","venue":"Journal of Environmental Monitoring","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Environment and Climate Change Canada; Institut National de la Recherche Scientifique","funders":"Fisheries and Oceans Canada","keywords":"Kriging; Sampling (signal processing); Principal component analysis; Data mining; Water quality; Fuzzy logic; Geostatistics; Computer science; Environmental science; Statistics; Mathematics; Machine learning; Artificial intelligence; Spatial variability","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009991055,0.0001712581,0.0002909469,0.00003805791,0.0001953475,0.00002644174,0.0001683036,0.00005939363,0.00001301021],"category_scores_gemma":[0.00003142974,0.0001078267,0.00006440467,0.00005385141,0.0002696829,0.0002738672,0.0001452298,0.0002593957,3.337287e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001896294,"about_ca_system_score_gemma":0.00001240441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000557602,"about_ca_topic_score_gemma":0.000001017776,"domain_scores_codex":[0.9984629,0.00003001294,0.0006139841,0.0001508112,0.0004116075,0.0003306576],"domain_scores_gemma":[0.9988686,0.0004285084,0.0004005025,0.0001566018,0.00001438768,0.0001314123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005053945,0.0001169628,0.3972498,0.00002288737,0.00006739463,3.829567e-7,0.0004752153,0.578862,0.0203746,0.00009005545,0.000001548825,0.002688567],"study_design_scores_gemma":[0.0005686063,0.000173981,0.7038811,0.0001197292,0.0001782384,0.0000175882,0.003889385,0.2839864,0.006903434,0.00007392681,0.00002402822,0.0001835666],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6576926,0.0002086652,0.3417388,0.00001730269,0.0001610982,0.0001400037,0.00001797632,0.000002440854,0.0000211783],"genre_scores_gemma":[0.8767158,0.0001332632,0.1229302,0.00000159713,0.0001731602,0.00001351934,0.000003947273,0.00001926691,0.00000923061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3066314,"threshold_uncertainty_score":0.4397043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07454190962658301,"score_gpt":0.3092075735137337,"score_spread":0.2346656638871507,"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."}}