{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004896069,0.0001080422,0.00009558085,0.00005813845,0.00005453819,0.00006073431,0.00005232814,0.00004364565,0.0000526636],"category_scores_gemma":[0.000004728517,0.00007592372,0.00002337368,0.00005433949,0.00002459436,0.0003089998,0.000005205608,0.0001169168,0.00009630548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001391375,"about_ca_system_score_gemma":0.000004839075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003965587,"about_ca_topic_score_gemma":0.00000770126,"domain_scores_codex":[0.9995088,0.00003150851,0.000103857,0.0001236346,0.00007911595,0.0001530873],"domain_scores_gemma":[0.9997039,0.0001526296,0.00001415018,0.0000515813,0.00001360118,0.00006407558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001090422,0.000004846325,0.0005311227,0.00001355294,0.0000461314,0.00002061177,0.007826689,0.0003761687,0.005495918,0.000935809,0.0004657734,0.9842725],"study_design_scores_gemma":[0.008628941,0.005995807,0.07983295,0.001781949,0.0002092489,0.001013823,0.004552645,0.2792241,0.01663652,0.2019412,0.395187,0.004995808],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7527652,0.005371918,0.235282,0.001832291,0.0006429275,0.0002516724,0.00001491881,0.0005878647,0.003251171],"genre_scores_gemma":[0.9986339,0.0001078844,0.0004518294,0.00007688437,0.00009773157,0.000008961451,0.000004671704,0.00001880299,0.0005993926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9792767,"threshold_uncertainty_score":0.309608,"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."}}