{"id":"W2258557027","doi":"10.4271/2004-01-1245","title":"High-Performance Machining Automation","year":2004,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automation; Machining; Computer science; Manufacturing engineering; Engineering; Mechanical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002875645,0.0006442467,0.0006049796,0.0002029724,0.000388908,0.0001058865,0.0006176055,0.000559453,0.0001802169],"category_scores_gemma":[0.0002466166,0.0006014644,0.0001686967,0.0007448978,0.0002969599,0.0009687237,0.0001569618,0.001068088,0.0001411186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004013772,"about_ca_system_score_gemma":0.0000561742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001642845,"about_ca_topic_score_gemma":0.0058725,"domain_scores_codex":[0.9970782,0.00003034309,0.0008232523,0.0007038395,0.0005993636,0.0007649714],"domain_scores_gemma":[0.9986367,0.000129619,0.0001419154,0.0007796573,0.0000749214,0.0002371951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008826733,0.0001097513,0.00004862619,0.0001400763,0.000029488,0.00001535522,0.00004481981,0.2501949,0.7176798,0.01663574,0.0002428358,0.01477033],"study_design_scores_gemma":[0.001284442,0.0008659354,0.9816677,0.0004868219,0.00006707556,0.00009183527,0.00004354931,0.00002357443,0.0009940875,0.004741837,0.008675749,0.001057401],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.932075,0.000662074,0.0005054212,0.001813672,0.0008566098,0.0009898385,0.00004622943,0.01580734,0.04724385],"genre_scores_gemma":[0.9610146,0.0008049248,0.03681958,0.0006439633,0.0001621688,0.0001865329,0.00009376207,0.0001715211,0.0001028931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9816191,"threshold_uncertainty_score":0.9996437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006343955684572088,"score_gpt":0.2228673209141722,"score_spread":0.2165233652296001,"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."}}