{"id":"W4414014551","doi":"10.3390/buildings15173212","title":"Generative AI for Architectural Façade Design: Measuring Perceptual Alignment Across Geographical, Objective, and Affective Descriptors","year":2025,"lang":"en","type":"article","venue":"Buildings","topic":"Aesthetic Perception and Analysis","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Workplace Health, Safety and Compensation Commission","funders":"","keywords":"Perception; Generative grammar; Generative model; Generative Design; Computer science; Artificial intelligence; Pattern recognition (psychology); Engineering; Psychology; Metric (unit)","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":[0.00033743,0.0002360533,0.0002701863,0.0001780909,0.000690604,0.0001837772,0.0001887277,0.00008629254,0.00002273629],"category_scores_gemma":[0.0003674506,0.0002036004,0.000179208,0.0004253889,0.0004286364,0.0001398906,0.00009626649,0.0001883325,0.000003736861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000762057,"about_ca_system_score_gemma":0.00003492339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007011343,"about_ca_topic_score_gemma":0.00002131492,"domain_scores_codex":[0.9982392,0.0002100061,0.0002049379,0.000680375,0.0002354108,0.000430034],"domain_scores_gemma":[0.9994484,0.0001517153,0.00006251526,0.0001607136,0.00006427204,0.0001124354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000139412,0.00008553171,0.001597036,0.00001984968,0.00002733996,0.00000298701,0.007286211,0.000583613,0.9639093,0.002542358,0.0005300143,0.02327629],"study_design_scores_gemma":[0.003278716,0.0007046123,0.02381115,0.000194181,0.000242721,0.00007966096,0.006533171,0.01838671,0.9306003,0.007742547,0.007278924,0.001147256],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7864985,0.0000448103,0.2116095,0.001054325,0.00009633796,0.0004511563,0.00001929457,0.00008313322,0.0001429086],"genre_scores_gemma":[0.9926832,0.00003495512,0.004001195,0.002696172,0.00004156031,0.0001537183,0.000001448576,0.00001679208,0.0003709388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2076083,"threshold_uncertainty_score":0.8302584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04261359110220098,"score_gpt":0.3131460372711753,"score_spread":0.2705324461689743,"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."}}