{"id":"W4251165863","doi":"10.46427/gold2020.1273","title":"Calculating Apportionment of Metals in PM<sub>2</sub><sub>.</sub><sub>5</sub> Using Ni Isotope Characterization","year":2020,"lang":"en","type":"article","venue":"Goldschmidt Abstracts","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Glencore (Canada); Queen's University","funders":"","keywords":"Apportionment; Characterization (materials science); Isotope; Environmental science; Materials science; Environmental chemistry; Chemistry; Nanotechnology; Nuclear physics; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007633567,0.0005469771,0.0007403454,0.0001586678,0.0002775181,0.0001039766,0.0002936745,0.0002508285,0.0001588486],"category_scores_gemma":[0.0003159013,0.0005709234,0.0002134901,0.0008399303,0.0002506687,0.001120774,0.0003420989,0.0004020597,0.0005776441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000336727,"about_ca_system_score_gemma":0.00007667579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008291843,"about_ca_topic_score_gemma":0.00006761106,"domain_scores_codex":[0.9956429,0.0001688386,0.001525855,0.000816123,0.001020881,0.0008253857],"domain_scores_gemma":[0.9979004,0.0001059361,0.001083062,0.000410493,0.00005705116,0.0004431184],"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":[0.00004175042,0.0001487025,0.0176433,0.00009297691,0.00006361397,0.00003048603,0.001792943,0.005806584,0.9619222,0.00001329066,0.0001736944,0.01227047],"study_design_scores_gemma":[0.0004639054,0.0000623695,0.3542493,0.00009712512,0.00006709048,0.000008796779,0.0002640978,0.001077681,0.6430815,0.00003703158,0.0001994275,0.0003917062],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996464,0.00007555502,0.0007956073,0.0004868885,0.0003334254,0.0008817565,0.00006807737,0.00009881319,0.0007958579],"genre_scores_gemma":[0.99817,0.0002367873,0.0002803363,0.0008306194,0.0001886691,0.00005040932,0.0001639318,0.00006862467,0.00001063883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.336606,"threshold_uncertainty_score":0.9996742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03233519765823783,"score_gpt":0.2502998917665025,"score_spread":0.2179646941082647,"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."}}