{"id":"W3195086207","doi":"10.5555/1480-6800.23.2.112","title":"Assessing Seasonal Variability, Trend, Distribution and Degree of Heavy Metal Contamination in Urban Lake Sediment, Malaysia","year":2020,"lang":"en","type":"article","venue":"Arab world geographer","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Sediment; Contamination; Distribution (mathematics); Environmental science; Geography; Degree (music); Heavy metals; Physical geography; Hydrology (agriculture); Ecology; Geology; Biology; Environmental chemistry; Geomorphology; Mathematics; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0002447025,0.0001651902,0.0001018319,0.0007812793,0.0001907149,0.0004263302,0.0001636818,0.0002003079,0.0003239725],"category_scores_gemma":[0.0002958429,0.0001409712,0.0001512547,0.0008014571,0.0001427836,0.0003013153,0.0002513976,0.0001337684,0.0001064562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004372093,"about_ca_system_score_gemma":0.0005710848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02772378,"about_ca_topic_score_gemma":0.05819963,"domain_scores_codex":[0.9999123,0.00001104538,0.00001336147,0.00002774252,0.00001669528,0.00001884575],"domain_scores_gemma":[0.9997595,0.00003268661,0.00009884623,0.00000897086,0.00006662786,0.00003337924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00013271,0.0000345049,0.9873427,0.0000261971,0.00003150883,0.00007329935,0.0004237175,0.0005975731,0.003883025,0.00003750075,0.00008389302,0.007333249],"study_design_scores_gemma":[0.000001206168,0.00007376994,0.9964383,0.000004553252,0.00002017598,0.00006170349,0.0008730689,0.001207264,0.001011209,0.00001703489,0.0002874199,0.000004354724],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995531,0.0000285221,0.00007273983,0.00000850889,6.77746e-7,0.00000213824,0.0001393052,0.000002561568,0.0001922849],"genre_scores_gemma":[0.9990526,0.00005188742,0.0001423792,0.000003541269,9.428487e-7,0.000002916832,0.000182094,7.26808e-7,0.000562975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02772378,"threshold_uncertainty_score":0.05512482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02844314214529471,"score_gpt":0.2694256774512692,"score_spread":0.2409825353059744,"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."}}