{"id":"W3194295085","doi":"","title":"Advances in NCS bulk isotope analysis: getting more with less","year":2020,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Nuclear Physics and Applications","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Thermo Fisher Scientific (Canada)","funders":"","keywords":"Isotope; Physics; Nuclear 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":[],"consensus_categories":[],"category_scores_codex":[0.0000897409,0.0001516663,0.0002291951,0.00004355748,0.0001059515,0.00006982311,0.000183445,0.00002264994,0.000006606473],"category_scores_gemma":[0.000006589361,0.0001371776,0.00007722107,0.0005550687,0.00003130445,0.0001751628,0.00004276181,0.0002141579,0.00003587208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009102874,"about_ca_system_score_gemma":0.00002582945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002110139,"about_ca_topic_score_gemma":0.000361564,"domain_scores_codex":[0.9989964,0.00001475098,0.0002557474,0.0003241936,0.0001550124,0.000253867],"domain_scores_gemma":[0.9994226,0.00006110463,0.0001865504,0.0001763879,0.00004473667,0.0001086438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002449671,0.0002384296,0.7283477,0.00004571306,0.0002443228,0.00001102247,0.002067505,0.2186146,0.001273225,0.005653259,0.000118127,0.04336163],"study_design_scores_gemma":[0.005224937,0.000364562,0.768315,0.001058166,0.001995558,0.000003149163,0.03222949,0.08525673,0.01498216,0.008705814,0.07723892,0.004625579],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9484051,0.00006848936,0.0004403722,0.0009764659,0.00001094182,0.0001324675,0.00001125777,0.00004384306,0.04991102],"genre_scores_gemma":[0.9981964,0.00000545651,0.001270151,0.000188946,0.0002310436,0.00002619845,0.00003582985,0.0000226629,0.00002327591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1333579,"threshold_uncertainty_score":0.5593941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218721296337396,"score_gpt":0.2489684891578694,"score_spread":0.2367812761944955,"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."}}