{"id":"W4285149062","doi":"10.5267/j.esm.2022.3.001","title":"Proposed method of forecasting cumulative effects of variation in manufacturing","year":2022,"lang":"en","type":"article","venue":"Engineering Solid Mechanics","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Variation (astronomy); Schedule; Product (mathematics); Computer science; Process (computing); Set (abstract data type); Manufacturing process; Industrial engineering; Manufacturing engineering; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001302743,0.0008909692,0.0009887745,0.002277197,0.0004510702,0.001603269,0.001712973,0.001330677,0.003238662],"category_scores_gemma":[0.004674724,0.0004266587,0.0008272443,0.002013911,0.0003266599,0.001168959,0.0007174652,0.001088608,0.0008609929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001149318,"about_ca_system_score_gemma":0.001470194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01433372,"about_ca_topic_score_gemma":0.008527241,"domain_scores_codex":[0.9992957,0.0001162775,0.00004302058,0.0002689682,0.000195005,0.00008106692],"domain_scores_gemma":[0.998478,0.0007089055,0.0001687835,0.000133716,0.0004480547,0.00006262506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002380893,0.000143034,0.01424612,0.0001168287,0.0001446574,0.0001977236,0.00009355689,0.7819373,0.003445975,0.005328111,0.001960115,0.1921484],"study_design_scores_gemma":[0.000004328279,0.00001951672,0.0008355127,0.000004647105,0.00001051321,0.00002359317,0.00001031718,0.9973422,0.0004274116,0.0009913375,0.0003230076,0.000007604818],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05380789,0.0003551326,0.9395211,0.0002745613,0.0001521387,0.0001822575,0.0008587503,0.00142101,0.003427246],"genre_scores_gemma":[0.7208384,0.000454738,0.2703618,0.0001479731,0.0001526456,0.0003804476,0.001362899,0.00009848594,0.006202759],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01433372,"threshold_uncertainty_score":0.02850062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01303263140094896,"score_gpt":0.2270087676777256,"score_spread":0.2139761362767766,"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."}}