{"id":"W2344438330","doi":"10.2172/1088459","title":"XNDL: METIS Partitioning Process","year":2013,"lang":"en","type":"report","venue":"","topic":"Radioactive element chemistry and processing","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Livermore National Laboratory; U.S. Department of Energy","keywords":"PUREX; Stripping (fiber); Reuse; Nuclear transmutation; Metis; Chemistry; Process (computing); Solvent extraction; Process engineering; Radiochemistry; Extraction (chemistry); Nuclear engineering; Materials science; Chromatography; Computer science; Waste management; Engineering; Physics; Operating system; Nuclear physics; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004168115,0.001129583,0.0005008591,0.0007041956,0.0006536075,0.001209608,0.001224699,0.0004035273,0.05551383],"category_scores_gemma":[0.0006910517,0.0004210385,0.0003070614,0.0005149625,0.0002948677,0.001062991,0.001175271,0.0008114011,0.02161326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009145609,"about_ca_system_score_gemma":0.00107273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003389086,"about_ca_topic_score_gemma":0.003540977,"domain_scores_codex":[0.9995449,0.0000378355,0.00001598382,0.0000798093,0.00026505,0.00005642706],"domain_scores_gemma":[0.9997368,0.00005474606,0.00001467505,0.00008693943,0.00009181906,0.00001508743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00187811,0.0003499808,0.00258611,0.001008443,0.00008339802,0.0004878875,0.0005024414,0.02861859,0.3436527,0.05899016,0.2045057,0.3573364],"study_design_scores_gemma":[0.0001386261,0.0001102367,0.0007181294,0.00003203637,0.00002028214,0.000258745,0.00005827692,0.0581125,0.6250122,0.003706952,0.3117975,0.00003444009],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.05637559,0.0003691124,0.6232262,0.0006917362,0.0003644685,0.00116932,0.02393905,0.1382482,0.1556163],"genre_scores_gemma":[0.3260907,0.0005998323,0.3924802,0.0003294184,0.00007643377,0.001335499,0.04457798,0.01538615,0.2191239],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05551383,"threshold_uncertainty_score":0.1857123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04511726784501096,"score_gpt":0.3323692933437501,"score_spread":0.2872520254987392,"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."}}