{"id":"W2964177407","doi":"10.2172/1542706","title":"Automated Particle Classifier Q3 FY19 Quarterly Report","year":2019,"lang":"en","type":"report","venue":"","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Livermore National Laboratory; U.S. Department of Energy","keywords":"Quarter (Canadian coin); Anticipation (artificial intelligence); Detector; Classifier (UML); Computer science; Artificial intelligence; History; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000397018,0.0003331613,0.0004683647,0.00009324196,0.00004796651,0.0001290697,0.0001526338,0.0004134909,0.0004315636],"category_scores_gemma":[0.0000419428,0.00028965,0.0001305637,0.00017845,0.0000187446,0.0001300749,0.00001638248,0.0004228418,0.0007218646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002066523,"about_ca_system_score_gemma":0.0002472969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001881599,"about_ca_topic_score_gemma":0.0000399675,"domain_scores_codex":[0.9980335,0.00001228653,0.0006203909,0.0003629844,0.0005387158,0.000432081],"domain_scores_gemma":[0.9990387,0.00002931059,0.0001222434,0.0005436049,0.000151634,0.0001145035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003691108,0.00003480168,0.002792913,0.001719679,0.0002947101,0.0008025823,0.0002726504,0.004583255,0.002977282,0.00003721747,0.9724854,0.01399587],"study_design_scores_gemma":[0.0003429634,0.00009447126,0.002986315,0.0005576991,0.0001386219,0.001532627,0.0001307523,0.265851,0.001067349,0.00004409139,0.725931,0.001323135],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1342133,0.0008643389,0.0007210759,0.00004939775,0.005042451,0.0002441604,0.0000143635,0.007175229,0.8516757],"genre_scores_gemma":[0.7508538,0.00005242166,0.0003409704,0.00001695291,0.0005548471,0.00002128703,0.000124652,0.0001274398,0.2479077],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6166405,"threshold_uncertainty_score":0.9999555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03387592283400312,"score_gpt":0.2853034973907397,"score_spread":0.2514275745567366,"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."}}