{"id":"W2191674269","doi":"10.1016/j.talanta.2015.10.050","title":"Automatic flow analysis method to determine traces of Mn2+ in sea and drinking waters by a kinetic catalytic process using LWCC-spectrophotometric detection","year":2015,"lang":"en","type":"article","venue":"Talanta","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Consejo Nacional de Ciencia y Tecnología; National Research Council Canada; Consejo Nacional de Ciencia y Tecnología, Paraguay; Ministerio de Economía y Competitividad; Federación Española de Enfermedades Raras","keywords":"Chemistry; Seawater; Tiron; Detection limit; Hydrogen peroxide; Repeatability; Certified reference materials; Catalysis; Flow injection analysis; Analytical Chemistry (journal); Manganese; Spectrophotometry; Kinetic energy; Chromatography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006138342,0.000237067,0.0005688746,0.0005412369,0.00003056516,0.00003806985,0.0002059688,0.0001152625,0.00006403879],"category_scores_gemma":[0.0003248549,0.0002225707,0.00007301952,0.00231645,0.00004581178,0.00008172578,0.00009164715,0.0001644706,0.000004258086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003154024,"about_ca_system_score_gemma":0.00005882578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002373479,"about_ca_topic_score_gemma":0.00003955648,"domain_scores_codex":[0.9981986,0.00005167893,0.0005276896,0.0004501073,0.0004240998,0.000347799],"domain_scores_gemma":[0.9990674,0.0001798773,0.0001650389,0.0002624161,0.00008317152,0.0002421509],"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":[0.00008672532,0.0001463141,0.05008745,0.0008398556,0.0004884757,0.00005579994,0.001843033,0.002190391,0.9203902,2.296654e-7,0.000006396725,0.02386513],"study_design_scores_gemma":[0.0003876209,0.00002020219,0.001047935,0.00006161993,0.000412369,0.00004539889,0.0003110624,0.2957136,0.7017359,0.00003626274,0.00001041953,0.0002175991],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9648522,0.0001071953,0.03459137,0.00001500816,0.00001206039,0.00009714714,0.00001442635,0.00004542376,0.0002652228],"genre_scores_gemma":[0.9546593,0.00000479316,0.04517844,0.00001070931,0.00001711395,0.00001794261,0.00002275843,0.0000234859,0.00006548422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2935232,"threshold_uncertainty_score":0.907617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03142214287949865,"score_gpt":0.3177404553456858,"score_spread":0.2863183124661872,"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."}}