{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05890919,0.0006294945,0.0007423278,0.00237641,0.004392017,0.006655454,0.002709903,0.00463529,0.002184473],"category_scores_gemma":[0.1869637,0.0008088,0.00111754,0.002762191,0.01029725,0.009136997,0.004084142,0.004147334,0.0005129502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005564637,"about_ca_system_score_gemma":0.00346152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00472921,"about_ca_topic_score_gemma":0.006385401,"domain_scores_codex":[0.9338274,0.04820859,0.00254115,0.002228884,0.01199626,0.001197725],"domain_scores_gemma":[0.7152447,0.2482623,0.008307461,0.01496688,0.01175166,0.00146706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.001014692,0.001697163,0.1283275,0.001573817,0.0001752471,0.02731123,0.4249844,0.02407408,0.007526346,0.1830769,0.01175828,0.1884803],"study_design_scores_gemma":[0.0002859048,0.001860156,0.03898348,0.004884196,0.0003755125,0.01627438,0.419445,0.09000497,0.0274861,0.1632005,0.2367403,0.0004594892],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8414503,0.0008346284,0.1044649,0.02399228,0.0001259256,0.0007074245,0.0004760697,0.0001623971,0.02778613],"genre_scores_gemma":[0.9585164,0.0003919246,0.03795749,0.0008120958,0.00002916184,0.0002773815,0.000172527,0.0001378296,0.001705167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05890919,"threshold_uncertainty_score":0.3115453,"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."}}