{"id":"W3045763157","doi":"10.1016/j.jglr.2020.07.004","title":"Copper-rich “Halo” off Lake Superior’s Keweenaw Peninsula and how Mass Mill tailings dispersed onto tribal lands","year":2020,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Environmental Protection Agency; National Science Foundation","keywords":"Tailings; Bay; Peninsula; Geology; Shore; Sediment; Mining engineering; Smelting; Copper; Oceanography; Environmental science; Geochemistry; Archaeology; Geomorphology; Geography; Metallurgy","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002368384,0.0002493112,0.000499633,0.0001179435,0.0002794681,0.0001929511,0.0006424674,0.0001603964,0.002400079],"category_scores_gemma":[0.0007852422,0.0001909595,0.0001495126,0.0004672765,0.0007471627,0.0004805364,0.0003936103,0.0009823436,0.0002176328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003120556,"about_ca_system_score_gemma":0.0000625572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000391704,"about_ca_topic_score_gemma":0.000202637,"domain_scores_codex":[0.9956234,0.0005646351,0.0004349237,0.0004400719,0.002200237,0.0007367565],"domain_scores_gemma":[0.9983659,0.0003046874,0.0001363442,0.0003016515,0.0000595938,0.0008318558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001016914,0.0002707987,0.5577927,0.00009558728,0.0001668052,0.001231638,0.005944759,0.0004298706,0.3844739,0.00002549012,0.03506243,0.01348912],"study_design_scores_gemma":[0.004583307,0.003154961,0.124887,0.0001107198,0.00009098862,0.0003957537,0.003285113,0.004278987,0.01965315,0.00009843334,0.8387642,0.0006973483],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981898,0.0006208894,0.00007511577,0.0137439,0.00009409208,0.0002835012,0.00002838646,0.00001330862,0.003242862],"genre_scores_gemma":[0.994543,0.001035595,0.001296743,0.0002479533,0.0002754672,0.000005207003,0.000004816392,0.00003837682,0.002552861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8037018,"threshold_uncertainty_score":0.9985119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04669293299568367,"score_gpt":0.3015298238892839,"score_spread":0.2548368908936002,"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."}}