{"id":"W2079033259","doi":"10.1007/s11270-008-9677-0","title":"Kinetic Speciation of Ni(II) in Model Solutions and Freshwaters: Competition of Al(III) and Fe(III)","year":2008,"lang":"en","type":"article","venue":"Water Air & Soil Pollution","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Acadia University; Carleton University; Trent University","funders":"","keywords":"Lability; Chemistry; Aqueous solution; Genetic algorithm; Dimethylglyoxime; Cathodic stripping voltammetry; Metal; Chelation; Inorganic chemistry; Ligand (biochemistry); Graphite furnace atomic absorption; Stripping (fiber); Atomic absorption spectroscopy; Nuclear chemistry; Environmental chemistry; Voltammetry; Cobalt; Mass spectrometry; Physical chemistry; Chromatography; Electrochemistry; Organic chemistry; Materials science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003374158,0.000287082,0.0004714554,0.0002543598,0.0004172887,0.0008124723,0.001035024,0.0007754623,0.002025779],"category_scores_gemma":[0.001305416,0.0002802123,0.0004459246,0.0002187379,0.000433053,0.001019743,0.0003638326,0.0005856954,0.0003464017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001599084,"about_ca_system_score_gemma":0.001027869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01478496,"about_ca_topic_score_gemma":0.006699746,"domain_scores_codex":[0.9998617,0.00001798942,0.00001279361,0.00004575731,0.00002204645,0.00003975036],"domain_scores_gemma":[0.9996752,0.0001436324,0.00003906157,0.00002401152,0.0000836911,0.00003451619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004426269,0.0004244095,0.01534803,0.00068215,0.0001576627,0.0005384228,0.0005437171,0.1130656,0.8269498,0.01837915,0.003421235,0.01606347],"study_design_scores_gemma":[0.0005653097,0.001083268,0.006508019,0.00003227798,0.0001168289,0.0003142356,0.0007868371,0.4774694,0.5003906,0.005985316,0.006566059,0.0001818748],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991788,0.0002340416,0.003809537,0.000392275,0.00005497309,0.00003671796,0.0006086502,0.0001175905,0.002958247],"genre_scores_gemma":[0.9965388,0.0001544616,0.001391053,0.00004659915,0.00000701514,0.00002079822,0.0003355652,0.00001536475,0.001490377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01478496,"threshold_uncertainty_score":0.02939779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01536582523389394,"score_gpt":0.2042641352578488,"score_spread":0.1888983100239549,"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."}}