{"id":"W2884082040","doi":"10.1007/s10661-018-6843-8","title":"Characterization of spatial and temporal patterns in surface water quality: a case study of four major Lebanese rivers","year":2018,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Conseil National de la Recherche Scientifique; Agence Universitaire de la Francophonie","keywords":"Water quality; Environmental science; Pollution; Hydrology (agriculture); Sampling (signal processing); Environmental monitoring; Surface water; Water pollution; Principal component analysis; Water resource management; Environmental engineering; Ecology; Geology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006358665,0.0002348694,0.0002107592,0.001244072,0.000684338,0.0008927861,0.0004751982,0.000452052,0.0004971725],"category_scores_gemma":[0.000768543,0.0001185476,0.0002895882,0.001689282,0.0005110754,0.0003806195,0.0005696146,0.0002106361,0.00005606053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001540943,"about_ca_system_score_gemma":0.0009404687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05953668,"about_ca_topic_score_gemma":0.08886684,"domain_scores_codex":[0.999671,0.000101791,0.0000257243,0.00006560836,0.00004666914,0.0000891446],"domain_scores_gemma":[0.9994074,0.0001883104,0.000162658,0.00004191091,0.0001549553,0.00004479944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001279037,0.0002553022,0.9674788,0.00004792757,0.00009417417,0.003717861,0.003783193,0.002285501,0.004966558,0.0006194624,0.0002589583,0.01636434],"study_design_scores_gemma":[0.000008739959,0.0001289403,0.9707956,0.0000227624,0.00006441851,0.0006813357,0.01726972,0.006655938,0.002037226,0.0002187826,0.002096328,0.00002020655],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999307,0.00003860365,0.0001829472,0.00002602595,8.476244e-7,0.000006674608,0.00006796138,0.000002204319,0.0003677237],"genre_scores_gemma":[0.9990778,0.0000659318,0.0004136049,0.000007376153,0.000001777729,0.000008525949,0.0001170355,0.000001514582,0.0003064712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05953668,"threshold_uncertainty_score":0.1183802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03284169488185246,"score_gpt":0.3020539641834812,"score_spread":0.2692122693016288,"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."}}