{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005031586,0.00113268,0.001083616,0.004164446,0.001400233,0.003454333,0.001440762,0.001181186,0.133268],"category_scores_gemma":[0.008172679,0.0005401825,0.0007814465,0.002714851,0.0002969633,0.001712536,0.001142104,0.001035803,0.154601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002914274,"about_ca_system_score_gemma":0.005288779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05576329,"about_ca_topic_score_gemma":0.0665049,"domain_scores_codex":[0.9962004,0.0002323609,0.000135712,0.000358649,0.002844223,0.0002286557],"domain_scores_gemma":[0.9882137,0.0009987085,0.0002477783,0.0009832111,0.009196066,0.0003605838],"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":[0.00009254223,0.00007543833,0.001356818,0.00006366598,0.00001342146,0.00001900982,0.00001068005,0.001184922,0.001217578,0.001015329,0.9126883,0.08226225],"study_design_scores_gemma":[0.000063018,0.0001728346,0.00937095,0.00007735551,0.0000223295,0.00007950064,0.00008038984,0.01627591,0.01139706,0.002271208,0.9601533,0.00003612261],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.01779584,0.001908018,0.08278113,0.006554605,0.007356924,0.002202981,0.4979982,0.03132496,0.3520774],"genre_scores_gemma":[0.02219098,0.0008904045,0.05637501,0.0003991665,0.000656119,0.0006476422,0.5175624,0.003033124,0.3982452],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.133268,"threshold_uncertainty_score":0.4458258,"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."}}