{"id":"W2801546051","doi":"10.1080/16864360.2018.1462571","title":"Machined sharp edge restoration for triangle mesh workpiece models derived from grid-based machining simulation","year":2018,"lang":"en","type":"article","venue":"Computer-Aided Design and Applications","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Machining; Grid; Enhanced Data Rates for GSM Evolution; Computer science; Mechanical engineering; Engineering drawing; Computational science; Materials science; Engineering; Geometry; Artificial intelligence; Mathematics","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":[0.0001412576,0.0001729509,0.000177334,0.00009947872,0.0002848623,0.0001393245,0.0001305124,0.00009725864,0.00001541077],"category_scores_gemma":[0.00001265441,0.0001831492,0.00004072018,0.0001524989,0.00003082666,0.0002245323,0.00001953858,0.00007403269,0.000007495982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003722198,"about_ca_system_score_gemma":0.00002248486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008606338,"about_ca_topic_score_gemma":0.000003602577,"domain_scores_codex":[0.9991522,0.00003266517,0.0002695787,0.0002944187,0.00008671135,0.0001644481],"domain_scores_gemma":[0.9990964,0.0004117223,0.00007651193,0.0002355434,0.0001000233,0.00007981582],"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":[0.00003510002,0.00002044772,0.000006375091,0.00003443918,0.00001607309,7.707835e-8,0.0001305431,0.9616837,0.0004630586,0.0006440112,0.0003911153,0.03657505],"study_design_scores_gemma":[0.0007469982,0.00005144101,0.000164275,0.00003528614,0.00003075265,1.843446e-7,0.000003469306,0.9885356,0.00332458,0.00512538,0.001781728,0.0002002963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003318663,0.0001522055,0.9947493,0.00007174466,0.0001465486,0.001004963,0.00003820692,0.0004293413,0.00008900131],"genre_scores_gemma":[0.6907075,0.00001199788,0.3078187,0.000127713,0.0005880033,0.0003763194,0.0003232473,0.00003346796,0.00001310293],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6873888,"threshold_uncertainty_score":0.7468606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04052498227543102,"score_gpt":0.2565029021801477,"score_spread":0.2159779199047167,"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."}}