{"id":"W2916219075","doi":"10.1051/matecconf/201926604003","title":"Analysis of Trace Metal Contamination in Pahang River and Kelantan River, Malaysia","year":2019,"lang":"en","type":"article","venue":"MATEC Web of Conferences","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Research, Innovation and Commercialization, University of Agriculture Faisalabad; Ministry of Higher Education, Malaysia; Universiti Tun Hussein Onn Malaysia","keywords":"Dredging; Environmental science; Contamination; Environmental chemistry; Trace metal; Sediment; Water quality; Heavy metals; Trace element; Atomic absorption spectroscopy; Metal; Chemistry; Geology; Ecology; Geochemistry; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001922556,0.0003041142,0.0001890278,0.001304457,0.0004867999,0.0007124348,0.0002160316,0.0004327742,0.0005944768],"category_scores_gemma":[0.0002083448,0.0001756079,0.0001928215,0.0008942756,0.0002265352,0.0002375505,0.0003338121,0.0001887069,0.0002253886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002513897,"about_ca_system_score_gemma":0.0004284414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005184243,"about_ca_topic_score_gemma":0.01040891,"domain_scores_codex":[0.9998028,0.00002407948,0.00002700949,0.00005358451,0.00006658228,0.00002596633],"domain_scores_gemma":[0.9998535,0.00001781526,0.00004628372,0.00000704048,0.0000567252,0.00001866173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005559016,0.0002045593,0.6694095,0.0007394833,0.0001413207,0.001327042,0.002911172,0.0009345677,0.263845,0.0003454087,0.0004064041,0.05917979],"study_design_scores_gemma":[0.00001182857,0.0006262314,0.9048398,0.0001149109,0.0001010367,0.002630859,0.005257512,0.001427601,0.07490079,0.0003104184,0.009738381,0.00004065291],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963028,0.0004990439,0.0007347386,0.00003139173,0.000006408081,0.00002019012,0.0005884819,0.00001821595,0.001798795],"genre_scores_gemma":[0.9934826,0.0005382247,0.001418026,0.00004872377,0.000003207872,0.00002761402,0.0005528352,0.000006801105,0.003921941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005184243,"threshold_uncertainty_score":0.01030809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009513217875277927,"score_gpt":0.2237428130931624,"score_spread":0.2142295952178845,"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."}}