{"id":"W1997408161","doi":"10.1039/c3ay41352g","title":"Optimization of solid phase extraction chromatography for the separation of Np from U and Pu using experimental design tools in complex matrices","year":2013,"lang":"en","type":"article","venue":"Analytical Methods","topic":"Radioactive element chemistry and processing","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Atomic Energy (Canada); Université Laval","funders":"","keywords":"Fractional factorial design; Factorial experiment; Actinide; Design of experiments; Fission products; Extraction (chemistry); Process engineering; Mathematics; Chromatography; Computer science; Biological system; Chemistry; Engineering; Statistics; Radiochemistry; Nuclear chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003155669,0.00009016307,0.000197919,0.00003946539,0.00005650234,0.0000343102,0.00006718552,0.00006868948,0.0004241349],"category_scores_gemma":[0.0001239395,0.00007302815,0.00005247338,0.0001491523,0.00008425354,0.0002953566,0.00001299278,0.00005959244,7.040877e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002152769,"about_ca_system_score_gemma":0.00001508419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004260677,"about_ca_topic_score_gemma":2.032572e-7,"domain_scores_codex":[0.9992606,0.00007243772,0.000324837,0.0001530949,0.00009137655,0.00009762859],"domain_scores_gemma":[0.9985304,0.001071891,0.0002067676,0.0000999982,0.00006229805,0.00002858644],"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":[0.00009645753,0.0001285873,0.0001855785,0.00006828475,0.00006288789,1.225047e-7,0.0001119295,0.009059672,0.9800031,0.00001738079,0.00002090117,0.01024507],"study_design_scores_gemma":[0.0003525968,0.00001626441,0.00004522994,0.0000159452,0.0000487556,9.086773e-7,0.0002328381,0.4821945,0.5169086,0.0001252953,0.00001732191,0.00004164746],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2540803,0.0003236286,0.7452216,0.00002621871,0.000008668672,0.000113942,0.00001534319,0.00000566365,0.0002046392],"genre_scores_gemma":[0.6106608,0.00001780779,0.3892306,0.000007522848,0.00003234956,0.0000192608,0.00002097064,0.000005588065,0.00000511309],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4731349,"threshold_uncertainty_score":0.464398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1717758722188127,"score_gpt":0.5026667943303708,"score_spread":0.330890922111558,"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."}}