{"id":"W2059074086","doi":"10.1039/c4ja00488d","title":"Use of Ga for mass bias correction for the accurate determination of copper isotope ratio in the NIST SRM 3114 Cu standard and geological samples by MC-ICPMS","year":2015,"lang":"en","type":"article","venue":"Journal of Analytical Atomic Spectrometry","topic":"Isotope Analysis in Ecology","field":"Environmental Science","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Merck KGaA","keywords":"NIST; Isotope; Analytical Chemistry (journal); Copper; Chemistry; Materials science; Environmental chemistry; Physics; Nuclear physics; Metallurgy; Computer 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":[0.002167818,0.00135491,0.0009824922,0.002173621,0.001465996,0.0009368209,0.002064139,0.00128757,0.003379542],"category_scores_gemma":[0.005451418,0.0006966405,0.0006946268,0.001747464,0.0007407707,0.0005813365,0.00103,0.001094082,0.002238133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008095723,"about_ca_system_score_gemma":0.002175723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007694358,"about_ca_topic_score_gemma":0.02723749,"domain_scores_codex":[0.9970741,0.0004806483,0.0002160491,0.0009396288,0.001108778,0.0001808168],"domain_scores_gemma":[0.9981841,0.0002651785,0.0001474499,0.0005051345,0.0008723498,0.00002571504],"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":[0.0003679296,0.00008380534,0.007100677,0.0004173238,0.0001829067,0.0003721434,0.0002798089,0.002069394,0.8907006,0.003552672,0.003901409,0.09097127],"study_design_scores_gemma":[0.00002281104,0.0001576984,0.01042242,0.00004653897,0.000163854,0.0006258887,0.00008454269,0.01773409,0.9315668,0.001540015,0.03756747,0.00006792911],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1859969,0.001625426,0.7833488,0.0003037497,0.00068164,0.0007468434,0.003540712,0.01068629,0.01306973],"genre_scores_gemma":[0.2465751,0.0006329062,0.73743,0.0002735263,0.00002406858,0.0007299243,0.002076399,0.001959579,0.01029862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007694358,"threshold_uncertainty_score":0.01529914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05918825001974172,"score_gpt":0.3037151158325034,"score_spread":0.2445268658127617,"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."}}