{"id":"W3103703588","doi":"10.52842/conf.acadia.2016.072","title":"What Bricks Want: Machine Learning and Iterative Ruin","year":2016,"lang":"en","type":"article","venue":"ACADIA quarterly","topic":"Architecture and Computational Design","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Tower; Artifact (error); Brick; Computer science; Capital (architecture); Artificial intelligence; Architectural engineering; Industrial engineering; Engineering; Archaeology; Civil engineering; History","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004491306,0.0005391141,0.0006488013,0.0007905049,0.0009679501,0.002223099,0.001412447,0.001689419,0.002363626],"category_scores_gemma":[0.02909365,0.0003773718,0.0005138837,0.0007263487,0.004704343,0.004276188,0.00155945,0.002501385,0.0002895579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001665967,"about_ca_system_score_gemma":0.001049336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003432184,"about_ca_topic_score_gemma":0.004140972,"domain_scores_codex":[0.9979832,0.001124124,0.00006768584,0.0002787025,0.0003121124,0.0002341808],"domain_scores_gemma":[0.9853247,0.01225472,0.0006709001,0.0006713405,0.0007977423,0.0002805641],"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":[0.0002378082,0.0003143624,0.02919013,0.0002886924,0.0001503195,0.0003890951,0.002596723,0.4785414,0.001241513,0.250331,0.0084245,0.2282945],"study_design_scores_gemma":[0.00001657491,0.00004028358,0.001301592,0.00005039263,0.00001378804,0.00004797139,0.0003291013,0.6960261,0.0006098,0.2993832,0.002162948,0.00001823418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.293218,0.002693847,0.6559156,0.01749202,0.0001290174,0.000121499,0.0001029639,0.0006344828,0.02969248],"genre_scores_gemma":[0.9266524,0.0003793192,0.06895481,0.0005016539,0.00006613472,0.0000789748,0.00007394795,0.00007397052,0.003218721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004491306,"threshold_uncertainty_score":0.02375257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004591038986663952,"score_gpt":0.1975509694090001,"score_spread":0.1929599304223361,"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."}}