{"id":"W2296181103","doi":"10.1111/cag.12253","title":"Using geovisualization to assess lead sediment contamination in Lake St. Clair","year":2016,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Water Quality and Resources Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Toronto Metropolitan University","funders":"","keywords":"Geovisualization; Bathymetry; Contamination; Environmental science; Sediment; Environmental remediation; Remote sensing; Cartography; Hydrology (agriculture); Geology; Geography; Visualization; Computer science; Geomorphology; Data mining; Ecology; Information visualization; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004589791,0.0003313021,0.0003166338,0.001957007,0.0004977594,0.00007092578,0.0003598756,0.0001504253,0.0006321808],"category_scores_gemma":[0.00009635614,0.0002688385,0.00012215,0.003424809,0.0008799363,0.0003662969,0.000117867,0.0001017591,0.00004124257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004921218,"about_ca_system_score_gemma":0.00004863569,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5181022,"about_ca_topic_score_gemma":0.9992505,"domain_scores_codex":[0.9972737,0.0001466432,0.0004259697,0.0006075269,0.0003339601,0.001212203],"domain_scores_gemma":[0.9986623,0.00009703033,0.0001018773,0.0003410423,0.00004635977,0.0007513989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001964675,0.00003477077,0.97939,0.00002375308,0.00005500698,0.00005835715,0.002615914,0.0003969992,0.000551851,0.001171815,0.007083273,0.008598639],"study_design_scores_gemma":[0.0006089901,0.0001527539,0.7117298,0.0001640407,0.00003526879,0.00001558679,0.004999196,0.00007264694,0.000238539,0.0008596309,0.2803542,0.0007693454],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945549,0.0001285013,0.0001748004,0.002250935,0.0003897266,0.0005125694,0.0002648001,0.00006072923,0.001663033],"genre_scores_gemma":[0.9978601,0.0002165548,0.00025869,0.0009074075,0.00006643605,0.00007430145,0.00005419424,0.00003499546,0.0005272758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4811484,"threshold_uncertainty_score":0.9999764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04192334771954446,"score_gpt":0.2436298822574889,"score_spread":0.2017065345379445,"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."}}