{"id":"W2059031742","doi":"10.1007/s11837-003-0159-y","title":"Exploiting model fidelity to control metals processing","year":2003,"lang":"en","type":"article","venue":"JOM","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Laurentian University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automation; Context (archaeology); Process (computing); Fidelity; Process control; Computer science; Systems engineering; Process modeling; Quality (philosophy); High fidelity; Control (management); Emerging technologies; Engineering; Manufacturing engineering; Risk analysis (engineering); Work in process; Mechanical engineering; Operations management; Artificial intelligence; Telecommunications; Business","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001737001,0.0001105816,0.0001824082,0.00004652292,0.00005228836,0.00003751216,0.00005861978,0.0000426458,0.000008077153],"category_scores_gemma":[0.0001633636,0.0001143563,0.00003331499,0.0001267791,0.000004117027,0.0002580552,0.000004645087,0.00006818165,0.00002689957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007749004,"about_ca_system_score_gemma":0.00001472398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002117279,"about_ca_topic_score_gemma":0.000003183797,"domain_scores_codex":[0.9992765,0.00002087138,0.0002390872,0.0001373751,0.0001123264,0.000213882],"domain_scores_gemma":[0.99966,0.00002453813,0.00003259628,0.0001449062,0.00006354778,0.00007441609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002014174,0.000003062362,0.00002149973,0.00002303607,0.000008993361,5.153121e-7,0.0001066424,0.9854683,0.01158074,0.0004679338,0.00006035165,0.002256882],"study_design_scores_gemma":[0.0003471649,0.000004500024,0.00001547396,0.00002400075,0.00001146936,0.000001805567,0.00004688443,0.9960981,0.002033786,0.0006116434,0.0006703525,0.0001348097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01094627,0.000317141,0.9816194,0.00003020399,0.00009030481,0.0002560386,0.000003990557,0.0003441795,0.006392499],"genre_scores_gemma":[0.9644946,0.000002036675,0.03506701,0.0001393375,0.00004604564,0.00008036543,0.000001221728,0.00003448618,0.0001348973],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9535483,"threshold_uncertainty_score":0.4663313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01294088279225324,"score_gpt":0.2303257100028659,"score_spread":0.2173848272106127,"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."}}