{"id":"W4388873776","doi":"10.1115/detc2023-116614","title":"Robust Design for Product Adaptation Considering Changes in Configurations and Parameters","year":2023,"lang":"en","type":"article","venue":"","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Product design; Tree (set theory); Product (mathematics); Adaptation (eye); Mathematical optimization; Node (physics); Probabilistic design; Design of experiments; Optimal design; Reliability engineering; Engineering design process; Mathematics; Engineering; Machine learning; Statistics","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":[0.0003681677,0.00007405084,0.00008031455,0.0002800425,0.00009896127,0.0001370307,0.00003483734,0.00001974017,0.00002040871],"category_scores_gemma":[0.000259145,0.00007113534,0.000007971678,0.0004540739,0.00001632068,0.0005031863,0.00002083932,0.00002374402,0.0000273665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009700242,"about_ca_system_score_gemma":0.00001493416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003650354,"about_ca_topic_score_gemma":0.0002814087,"domain_scores_codex":[0.99948,0.000005040478,0.000115863,0.000194113,0.00006313964,0.000141837],"domain_scores_gemma":[0.9997613,0.00005615653,0.00005178449,0.00005929263,0.00006737919,0.000004077266],"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":[0.0005141183,0.0002022983,0.03517044,0.001812465,0.0001747957,0.00002212657,0.005238312,0.4004273,0.0299205,0.179649,0.1015576,0.2453111],"study_design_scores_gemma":[0.002003354,0.00002011272,0.02403474,0.00009714154,0.0000558524,0.000001477774,0.004178343,0.9271002,0.006468161,0.01608097,0.01927136,0.0006882399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.707867,0.0001269725,0.2430552,0.03407406,0.001576229,0.005601153,0.000003551802,0.001400719,0.006295187],"genre_scores_gemma":[0.9852294,0.00002227546,0.01298006,0.0005726924,0.0002107003,0.0002454018,0.00009970065,0.00001773762,0.000621976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5266729,"threshold_uncertainty_score":0.2900815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1286136170285858,"score_gpt":0.2370098218928794,"score_spread":0.1083962048642936,"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."}}