{"id":"W2059761966","doi":"10.1007/s00216-006-0759-9","title":"Kinetic speciation of nickel in mining and municipal effluents","year":2006,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Acadia University; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nickel; Genetic algorithm; Environmental chemistry; Effluent; Chemistry; Environmental science; Biochemical engineering; Environmental engineering; Engineering; Biology; Organic chemistry; Ecology","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":[0.0002204655,0.0001320038,0.0002379893,0.00002070154,0.00002395543,0.00001217147,0.00009249648,0.0001074592,0.0007926337],"category_scores_gemma":[0.00008818816,0.0001137483,0.00004166353,0.0001827707,0.0005866431,0.00005004995,0.0001878458,0.0001032507,0.00001481469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006412542,"about_ca_system_score_gemma":0.000003278445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000286852,"about_ca_topic_score_gemma":0.0000466526,"domain_scores_codex":[0.9987617,0.00002160047,0.0003661808,0.000321505,0.0002805925,0.0002484056],"domain_scores_gemma":[0.9995573,0.0001053754,0.00004948305,0.0001614199,0.000002828142,0.0001236176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003658158,0.0003924051,0.955883,0.0000767088,0.00002409601,0.00002826213,0.00004929446,0.0002388296,0.03931035,0.0002501949,0.0002001752,0.003510141],"study_design_scores_gemma":[0.0008795816,0.00005894697,0.9018255,0.00005177545,0.0001204998,0.00001808316,0.000110104,0.07017039,0.02386927,0.001051961,0.001487302,0.0003565341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982901,0.00007559478,0.00002091876,0.0002203779,0.000006318781,0.00004699629,0.000003117221,0.000005705296,0.01671998],"genre_scores_gemma":[0.9984322,0.00003606549,0.0004928798,0.00004507345,0.00003206079,0.00000194337,0.000004871858,0.000006481368,0.0009483934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06993156,"threshold_uncertainty_score":0.8678783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008009780133003375,"score_gpt":0.2222879094521041,"score_spread":0.2142781293191007,"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."}}