{"id":"W4309468518","doi":"10.1115/1.4056284","title":"Special Issue: Manufacturing Science Engineering Conference 2022","year":2022,"lang":"en","type":"article","venue":"Journal of Manufacturing Science and Engineering","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Engineering; Library science; Government (linguistics); Manufacturing engineering; Political science; Computer science; Law","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003936609,0.003959153,0.002334504,0.005011059,0.002537995,0.01300514,0.002863652,0.006488795,0.3406826],"category_scores_gemma":[0.006049503,0.0008615942,0.002054982,0.002487168,0.0007428696,0.004831462,0.003235174,0.004823286,0.2255742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00202215,"about_ca_system_score_gemma":0.003932884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006556279,"about_ca_topic_score_gemma":0.001559044,"domain_scores_codex":[0.9962385,0.0004303119,0.0003636714,0.000510828,0.001978277,0.0004784279],"domain_scores_gemma":[0.9901322,0.0008708198,0.0006043497,0.0004535691,0.00523462,0.002704406],"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":[0.00003846002,0.00004378278,0.00008246631,0.0002558204,0.00001103901,0.00009166355,0.000008149897,0.00004611032,0.0002779882,0.0004029741,0.9786574,0.02008415],"study_design_scores_gemma":[0.00001502149,0.00004181149,0.0004701281,0.0001851768,0.00000667581,0.0001207928,0.00002564063,0.0001040336,0.0001630013,0.0004096342,0.9984475,0.0000105152],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0008223969,0.0156473,0.001323346,0.02199558,0.8187663,0.000479377,0.001959375,0.00111599,0.1378903],"genre_scores_gemma":[0.003798175,0.01719966,0.001277184,0.01192779,0.4377636,0.0005215926,0.006241524,0.001013215,0.5202573],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.3406826,"threshold_uncertainty_score":0.9404362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01052750606508145,"score_gpt":0.2034377948184538,"score_spread":0.1929102887533723,"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."}}