{"id":"W2963675373","doi":"10.14796/jwmm.c465","title":"Spatial Patterns of Heavy Metals in the Sediments of a Municipal Wastewater Treatment Pond System and Receiving Waterbody, Cha am, Thailand","year":2019,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wastewater; Environmental science; Heavy metals; Sewage treatment; Environmental engineering; Aeration; Water resource management; Hydrology (agriculture); Waste management; Geology; Engineering; Environmental chemistry; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.0001516884,0.0002579769,0.0002170192,0.0009432777,0.0004432935,0.0006366628,0.0001943321,0.0002152753,0.0004964872],"category_scores_gemma":[0.0004272034,0.0002399807,0.0002589345,0.002458427,0.000464445,0.0002320428,0.0007050907,0.0001502467,0.0001110201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004474235,"about_ca_system_score_gemma":0.0005175711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02897019,"about_ca_topic_score_gemma":0.04248748,"domain_scores_codex":[0.9997743,0.00002998058,0.00002503051,0.00007934769,0.00005508391,0.00003636955],"domain_scores_gemma":[0.9996942,0.000032375,0.0001177346,0.00001470203,0.00009867063,0.00004227557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001967139,0.0000412819,0.9685168,0.0001196342,0.00008620197,0.0006823814,0.002498018,0.0007293195,0.01648873,0.00005622884,0.0001451346,0.01043951],"study_design_scores_gemma":[0.000002642316,0.00004257082,0.9961661,0.000005452792,0.000017764,0.000157449,0.002195342,0.0004363311,0.0006840968,0.00001526668,0.000269934,0.000007006694],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994174,0.000025627,0.0001058108,0.000005783011,5.895268e-7,0.000005554571,0.0001578425,0.000005685204,0.0002757138],"genre_scores_gemma":[0.9989641,0.00004620755,0.0002814086,0.000004803072,0.000001121931,0.00001339548,0.0003050106,0.000001945405,0.0003821212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02897019,"threshold_uncertainty_score":0.05760312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01770595322226887,"score_gpt":0.2279633706200378,"score_spread":0.210257417397769,"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."}}