{"id":"W4285717939","doi":"10.52842/conf.caadria.2020.1.557","title":"What do Design Data say About Your Model? - A Case Study on Reliability and Validity","year":2020,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mitacs; Boeing","keywords":"Computer science; Cohesion (chemistry); Workflow; Data science; Parametric design; Reliability (semiconductor); Analytics; Parametric statistics; Software engineering; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003683681,0.0002634315,0.0003117308,0.0003447507,0.0002112833,0.001654181,0.005172799,0.00005475505,0.000007871425],"category_scores_gemma":[0.0009128122,0.0001903682,0.00005314409,0.0007639388,0.0002256387,0.001241416,0.003488272,0.0007930181,0.000007229008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009703508,"about_ca_system_score_gemma":0.0002316158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000553887,"about_ca_topic_score_gemma":0.000005399313,"domain_scores_codex":[0.9959121,0.0004117682,0.000549807,0.001124375,0.001613693,0.0003882771],"domain_scores_gemma":[0.9973861,0.0007359751,0.0002199158,0.0006508099,0.0007799261,0.0002272887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003154806,0.005489919,0.00830647,0.0007121129,0.0005426938,0.001353304,0.05009327,0.1600265,0.01187903,0.4057173,0.01492412,0.3378004],"study_design_scores_gemma":[0.000590359,0.0009010058,0.000493995,0.0003322582,0.000005201276,0.0001208799,0.0008934796,0.9757451,0.0009774622,0.01972616,0.00002663504,0.0001874768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3898971,0.00002250435,0.5805616,0.02652352,0.0003733217,0.002080256,0.00004725859,0.0001193936,0.0003749519],"genre_scores_gemma":[0.9623213,0.00005203552,0.03702275,0.0004420149,0.00008799135,0.00002727757,0.000004623817,0.00001384464,0.00002809946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8157186,"threshold_uncertainty_score":0.9993822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4747671265344694,"score_gpt":0.4446143766170649,"score_spread":0.03015274991740446,"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."}}