{"id":"W2154659082","doi":"10.1007/s11837-012-0433-y","title":"A Database Approach for Predicting and Monitoring Baked Anode Properties","year":2012,"lang":"en","type":"article","venue":"JOM","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alcoa (Canada); Université Laval","funders":"Alcoa","keywords":"Anode; Multivariate statistics; Raw material; Population; Process engineering; Computer science; Database; Engineering; Machine learning","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":[0.0007972494,0.0007771942,0.001796228,0.002790372,0.0005735991,0.002417734,0.002278556,0.0012882,0.00119509],"category_scores_gemma":[0.002500792,0.0004921495,0.0008964445,0.00230328,0.0003168182,0.002290889,0.0007476927,0.0006379569,0.0007070781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007200447,"about_ca_system_score_gemma":0.0009794972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01211101,"about_ca_topic_score_gemma":0.01093781,"domain_scores_codex":[0.9992755,0.00006653638,0.0001094433,0.0002489471,0.0002506597,0.00004895565],"domain_scores_gemma":[0.9984517,0.0005165474,0.0001455821,0.0003728257,0.0004479548,0.00006528588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001072843,0.0009592262,0.0352973,0.0004606664,0.0006315859,0.0006864433,0.0002139062,0.3054762,0.03427183,0.008959102,0.005455134,0.6065158],"study_design_scores_gemma":[0.00002396554,0.00015186,0.003187244,0.0000186553,0.0001387357,0.0001640434,0.00006187202,0.9790226,0.00892684,0.006111084,0.002164965,0.0000281198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1028847,0.001721735,0.8827543,0.0002675913,0.00009875974,0.0001769018,0.004943366,0.005663102,0.001489466],"genre_scores_gemma":[0.6981695,0.001043086,0.2933455,0.0001545934,0.00007880203,0.0002486881,0.005387081,0.00008371483,0.001489051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01211101,"threshold_uncertainty_score":0.02408105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02998097268584693,"score_gpt":0.221260897799003,"score_spread":0.191279925113156,"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."}}