{"id":"W3211808474","doi":"10.1115/detc2021-72102","title":"Identifying Computer-Aided Design Action Types From Professional User Analytics Data","year":2021,"lang":"en","type":"article","venue":"","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"CAD; Computer science; Leverage (statistics); Cloud computing; Analytics; Computer Aided Design; Point cloud; Data science; Human–computer interaction; Artificial intelligence; Engineering; Engineering drawing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00352756,0.0006021609,0.0003527822,0.007752785,0.0005621262,0.001162855,0.0006003024,0.0005597291,0.001738145],"category_scores_gemma":[0.02089568,0.000205411,0.0004693018,0.004187773,0.0004308849,0.001017183,0.001114834,0.0004730586,0.0007823332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006918857,"about_ca_system_score_gemma":0.0007471471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005030915,"about_ca_topic_score_gemma":0.009021161,"domain_scores_codex":[0.996285,0.001527725,0.0004411921,0.0005312103,0.0009120373,0.0003029112],"domain_scores_gemma":[0.9695629,0.01751971,0.004883024,0.002983595,0.004050274,0.001000441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002463998,0.0002425274,0.899969,0.0002742778,0.00005951835,0.0001045576,0.005035755,0.002260604,0.004153524,0.0008364962,0.001429035,0.0853883],"study_design_scores_gemma":[0.00001691194,0.0003392414,0.9084652,0.0001129827,0.00003624482,0.0003347506,0.008703292,0.06839415,0.004115805,0.003092194,0.006313094,0.00007612242],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661758,0.00009625989,0.0258702,0.0001395404,0.0000105765,0.0003018523,0.004057538,0.0003122752,0.003036005],"genre_scores_gemma":[0.9664766,0.00004154354,0.02908565,0.00002156912,0.000006248875,0.0003046902,0.003417497,0.00002058151,0.0006256884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007752785,"threshold_uncertainty_score":0.01865578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1125445270245559,"score_gpt":0.3012347039447509,"score_spread":0.188690176920195,"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."}}