{"id":"W2793631480","doi":"10.1007/s10967-018-5758-8","title":"An ion chromatographic separation method for the sequential determination of 90Sr, 241Am and Pu isotopes in a urine sample","year":2018,"lang":"en","type":"article","venue":"Journal of Radioanalytical and Nuclear Chemistry","topic":"Radioactive contamination and transfer","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Health Canada","funders":"","keywords":"Cartridge; Chromatography; Chemistry; Extraction (chemistry); Radionuclide; Chromatographic separation; Sample preparation; Isotope; Sample (material); Urine; Solid phase extraction; Detection limit; Urine sample; Radiochemistry; High-performance liquid chromatography; 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":[0.0007237918,0.0009181467,0.0006210722,0.001497929,0.001308368,0.0004619177,0.00115904,0.001262642,0.001423106],"category_scores_gemma":[0.001006536,0.0005229199,0.0005213965,0.000615373,0.0005596349,0.0004955081,0.0005318284,0.001100783,0.001408668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00043493,"about_ca_system_score_gemma":0.001962663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001852478,"about_ca_topic_score_gemma":0.003536271,"domain_scores_codex":[0.999401,0.00008037628,0.00003207871,0.0001556007,0.0002846631,0.00004634199],"domain_scores_gemma":[0.9993944,0.0001688191,0.00005724001,0.00003531385,0.0002377865,0.000106484],"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":[0.0002666847,0.0001106603,0.0008424959,0.0001446855,0.00004408822,0.000253036,0.00005216487,0.00007059098,0.9675579,0.000210029,0.0005774103,0.02987024],"study_design_scores_gemma":[0.00006944702,0.0007816636,0.005014696,0.00003292999,0.0001857418,0.005813176,0.00006257762,0.002896665,0.971535,0.0002133433,0.01330893,0.00008574729],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3393083,0.0135935,0.6290529,0.001323189,0.001383664,0.001203778,0.0009380165,0.004984515,0.008212075],"genre_scores_gemma":[0.5513721,0.007095821,0.4202134,0.002316812,0.0003553607,0.0007603953,0.0009213916,0.0002873666,0.01667749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001852478,"threshold_uncertainty_score":0.004760802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01032760996850298,"score_gpt":0.2918213247261752,"score_spread":0.2814937147576722,"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."}}