{"id":"W1981977350","doi":"10.4028/www.scientific.net/amr.188.584","title":"Robotic High Speed Machining of Aluminum Alloys","year":2011,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Machining; Rigidity (electromagnetism); Vibration; Mechanical engineering; Stiffness; Stability (learning theory); Acceleration; Process (computing); Robot; Engineering; Computer science; Structural engineering; Acoustics; Artificial intelligence; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004635881,0.0001538072,0.0002877032,0.0001857838,0.0000838507,0.00002383946,0.0002763632,0.00007959482,0.0006955855],"category_scores_gemma":[0.000140772,0.0001516558,0.00002466319,0.0003047489,0.00009357094,0.0003144215,0.00009702185,0.0001766331,0.00004109106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004196609,"about_ca_system_score_gemma":0.00002132012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006316241,"about_ca_topic_score_gemma":0.000005513923,"domain_scores_codex":[0.9985901,0.0000602097,0.0003523302,0.0002254782,0.0003070117,0.000464906],"domain_scores_gemma":[0.999294,0.00008515362,0.00005391994,0.0003033249,0.0001819012,0.00008165892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001354524,0.000046378,0.00005753359,0.0003778735,0.00003520798,0.000009277715,0.0007421913,0.2801113,0.7084075,0.003768937,0.0000822629,0.006226139],"study_design_scores_gemma":[0.0005622234,0.0001691007,0.0005293168,0.0001295076,0.000009938167,0.000005602209,0.0001730396,0.004822385,0.9868115,0.006199981,0.0003336244,0.000253844],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9176,0.0006086564,0.06469497,0.00002443143,0.001620344,0.00060473,0.00003769951,0.000543815,0.01426533],"genre_scores_gemma":[0.9494888,0.0004030059,0.04967542,0.000006190279,0.00007884899,0.0000209672,0.00002882362,0.00006595493,0.0002319621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.278404,"threshold_uncertainty_score":0.7616173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05689940003803647,"score_gpt":0.3162820767254041,"score_spread":0.2593826766873677,"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."}}