{"id":"W3128026765","doi":"10.1115/1.2164452","title":"Modeling of Evolutionary Design Database","year":2005,"lang":"en","type":"article","venue":"Journal of Computing and Information Science in Engineering","topic":"Design Education and Practice","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Consistency (knowledge bases); Ancestor; Descendant; Database; Generative Design; Database design; Conceptual design; Artificial intelligence; Engineering; Human–computer interaction","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":[0.001237704,0.00004766338,0.00008254255,0.0005310258,0.00002761817,0.00003707985,0.0001123352,0.00001587615,0.000003092189],"category_scores_gemma":[0.0002646799,0.00004668583,0.00001219184,0.0004151147,0.00001721306,0.004985597,0.00001396573,0.0001350627,0.000001523408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005799376,"about_ca_system_score_gemma":0.00007094807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000116057,"about_ca_topic_score_gemma":3.754335e-8,"domain_scores_codex":[0.9992368,0.00000703249,0.0004263393,0.00002491786,0.0002055976,0.00009926359],"domain_scores_gemma":[0.9995871,0.00009048187,0.00008009891,0.00004977972,0.0001420622,0.00005044383],"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":[0.000002057258,0.000003785377,0.00003534145,0.00002536673,0.0000013565,1.229205e-7,0.0009808331,0.990523,0.0009000296,0.0002152236,0.00003138184,0.007281471],"study_design_scores_gemma":[0.0001168222,0.00001115605,0.000637432,0.00008774052,0.000001886807,0.00005530543,0.0002196466,0.9975127,0.0005574202,0.000005336152,0.000747255,0.00004733081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2381824,0.0001453386,0.7609546,0.00004411323,0.0002606199,0.0000251799,3.090722e-7,0.00001480802,0.0003726717],"genre_scores_gemma":[0.9125229,0.00008978199,0.08732202,0.00001710828,0.00004522244,1.495902e-7,2.272744e-7,0.000002111599,4.640999e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6743405,"threshold_uncertainty_score":0.3614437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02044037714741485,"score_gpt":0.2664097345006112,"score_spread":0.2459693573531963,"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."}}