{"id":"W2091748074","doi":"10.1016/s0007-8506(07)60694-5","title":"Model-Based Monitoring and Control of Continuous Dress Creep-Feed Form Grinding","year":2004,"lang":"en","type":"article","venue":"CIRP Annals","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Pratt and Whitney Canada","keywords":"Grinding; Serration; Power (physics); Mechanical engineering; Heat flux; Materials science; Flux (metallurgy); Process (computing); Turbine; Structural engineering; Mechanics; Engineering; Computer science; Metallurgy; Composite material; Heat transfer","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000731191,0.0001037366,0.00018116,0.00006266876,0.00005105501,0.00002139189,0.00006278623,0.00005235009,0.000001671756],"category_scores_gemma":[0.00003197524,0.0001085745,0.00003006778,0.0000757976,0.00002231924,0.0001563563,0.00000830011,0.00007624917,5.871144e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000154237,"about_ca_system_score_gemma":0.00001286515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004312428,"about_ca_topic_score_gemma":0.000001141099,"domain_scores_codex":[0.9994676,0.000002953866,0.0001683118,0.0001041335,0.00008329958,0.0001736866],"domain_scores_gemma":[0.9997091,0.0000268773,0.00004973882,0.00009357078,0.00006650867,0.00005426213],"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":[0.00001282005,0.000009107704,0.000929259,0.000108485,0.00001453996,0.000001369519,0.00025121,0.9892265,0.005659061,0.0002821275,0.000001942613,0.003503614],"study_design_scores_gemma":[0.002233031,0.00006993828,0.002114624,0.0002850133,0.000031542,0.000002683972,0.0001230273,0.8940465,0.09783312,0.002925626,0.00005105749,0.0002838808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3028657,0.0006920234,0.695724,0.00003250494,0.0000806009,0.00009076478,0.000008433874,0.0001089687,0.0003969493],"genre_scores_gemma":[0.9933206,0.0001083321,0.006451809,0.000022201,0.00004610038,0.00001118099,0.0000033394,0.00002491549,0.00001146355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6904549,"threshold_uncertainty_score":0.442754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01952733686979308,"score_gpt":0.2630407357534,"score_spread":0.2435133988836069,"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."}}