{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002102148,0.000433842,0.000256237,0.000756551,0.000367759,0.0003796863,0.0005077012,0.0003690594,0.002180974],"category_scores_gemma":[0.0005015468,0.0002752631,0.000522961,0.000515068,0.0001103379,0.0003783337,0.0002220506,0.0001777917,0.0008137499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007865162,"about_ca_system_score_gemma":0.0003818623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01465246,"about_ca_topic_score_gemma":0.02068106,"domain_scores_codex":[0.9998251,0.00001131423,0.000008656002,0.00006475993,0.00007242738,0.00001768743],"domain_scores_gemma":[0.9998827,0.00003552023,0.00001192321,0.00001600232,0.00004816991,0.000005750136],"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":[0.001318348,0.0001535693,0.2365572,0.0003715299,0.0003302243,0.000521264,0.0004667496,0.07796137,0.524746,0.002986575,0.001525201,0.1530619],"study_design_scores_gemma":[0.00003167693,0.0002184745,0.1257734,0.00001662516,0.000203649,0.0003435426,0.000323211,0.2346097,0.6302927,0.001738423,0.00639708,0.00005153269],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9515151,0.000200676,0.03888983,0.00005658228,0.0000213298,0.00006153933,0.001584564,0.0007893811,0.006880936],"genre_scores_gemma":[0.9742509,0.000138988,0.0209185,0.0000272379,0.000003752961,0.00004405083,0.0008085473,0.000109613,0.003698526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01465246,"threshold_uncertainty_score":0.02913433,"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."}}