{"id":"W2888840615","doi":"10.1007/s10661-018-6927-5","title":"Evaluation of surface water quality by using Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) method and discriminant analysis method: a case study Coruh River Basin","year":2018,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":139,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Water quality; Ecotoxicology; Index (typography); Environmental science; Linear discriminant analysis; Structural basin; Surface water; Quality (philosophy); Drainage basin; Council of Ministers; Hydrology (agriculture); Water resource management; Environmental engineering; Geography; Environmental chemistry; Geology; Statistics; Ecology; Mathematics; Chemistry; European union; Computer science; Cartography; Biology; Geomorphology","routes":{"ca_aff":false,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.009594045,0.0002906228,0.0005493145,0.00005612858,0.0003470144,0.00002660874,0.0001855546,0.00009644152,0.0004375487],"category_scores_gemma":[0.00001142277,0.0001871249,0.0001321947,0.00009943928,0.0007288249,0.0001787854,0.0004318524,0.0001583967,0.000002518619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001992767,"about_ca_system_score_gemma":0.00008025132,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2751109,"about_ca_topic_score_gemma":0.004425201,"domain_scores_codex":[0.9935047,0.002786458,0.0008735076,0.000573945,0.00188052,0.0003808586],"domain_scores_gemma":[0.9987563,0.0001029744,0.0003556489,0.0005620953,0.00002298646,0.0001999404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002493714,0.0005235908,0.7398396,0.00002605111,0.0004081021,0.000004034633,0.01709419,0.004030674,0.2348299,0.000002128866,0.000003415153,0.003213412],"study_design_scores_gemma":[0.001014066,0.0002378061,0.7909344,0.00001731918,0.001419031,0.00001321437,0.01325158,0.004662026,0.1878971,0.00005055514,0.0002037699,0.0002991529],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950775,0.00003529734,0.003723316,0.000104006,0.0001420983,0.0006351256,0.0001856765,0.0000039094,0.00009310194],"genre_scores_gemma":[0.993857,0.000009887839,0.005956167,0.00001627445,0.00001683492,0.00001762505,0.00001035619,0.00001503776,0.0001007705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2706857,"threshold_uncertainty_score":0.7630732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1405386507924078,"score_gpt":0.400338909321411,"score_spread":0.2598002585290031,"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."}}