{"id":"W2899531497","doi":"10.1016/j.scitotenv.2018.11.065","title":"Controls governing the spatial distribution of sediment arsenic concentrations and solid-phase speciation in a lake impacted by legacy mining pollution","year":2018,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Impact; Government of Northwest Territories; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Montréal","keywords":"Sediment; Pollution; Arsenic; Environmental science; Genetic algorithm; Spatial distribution; Environmental chemistry; Environmental engineering; Water resource management; Geology; Ecology; Geomorphology; Remote sensing; Chemistry; Metallurgy; Materials science; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008501657,0.00008805753,0.00009480616,0.00001370394,0.0003630389,0.00002763065,0.0002293759,0.00002581433,0.0002397808],"category_scores_gemma":[0.00011765,0.00005101166,0.00003502916,0.0002217627,0.001921303,0.0002992709,0.0001885509,0.0000734997,0.00001047459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003013356,"about_ca_system_score_gemma":0.00003062818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002069457,"about_ca_topic_score_gemma":0.0001629289,"domain_scores_codex":[0.9987041,0.0001033786,0.0002824959,0.0001808484,0.0005381838,0.0001910119],"domain_scores_gemma":[0.9993541,0.00005456354,0.0003068303,0.0002357593,0.000008389078,0.00004032917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00006117069,0.0001810107,0.0009667719,0.000002082098,0.00001016336,1.288614e-7,0.004350654,0.007440031,0.9754677,0.0004669465,0.0002553262,0.01079806],"study_design_scores_gemma":[0.00191595,0.000338894,0.73836,0.00003853844,0.00005478308,0.000008241994,0.00209565,0.1133307,0.1426044,0.0002020551,0.0008858418,0.0001649923],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955573,0.0000182823,0.001376337,0.002090916,0.00009169539,0.0004346459,0.00006983507,0.00000419208,0.0003567979],"genre_scores_gemma":[0.9996995,0.00001286809,0.00003077428,0.00003481885,0.0000249027,0.00001012983,0.00001111547,0.000003070741,0.00017283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8328632,"threshold_uncertainty_score":0.7079121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006006035278921107,"score_gpt":0.2394953006899299,"score_spread":0.2334892654110088,"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."}}