{"id":"W2057053708","doi":"10.1016/j.autcon.2014.10.007","title":"Exploring cellular automata for high density residential building form generation","year":2014,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Cellular Automata and Applications","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Cellular automaton; Computer science; Context (archaeology); Diagrammatic reasoning; Adaptation (eye); Process (computing); Architectural design; Architectural engineering; Architectural model; Theoretical computer science; Architecture; Programming language; Engineering; Artificial intelligence; Geography","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":[0.0005678193,0.0001388983,0.0001597017,0.0002827889,0.000314245,0.0002467399,0.0003164573,0.00007705355,0.000006132672],"category_scores_gemma":[0.00009682737,0.0001614995,0.00005722748,0.0004386789,0.00003751602,0.001777026,0.00008886908,0.00008576303,0.00002177611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001243384,"about_ca_system_score_gemma":0.00005054677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001286718,"about_ca_topic_score_gemma":0.00005626202,"domain_scores_codex":[0.9985836,0.00007252161,0.0004378814,0.0004340913,0.00023921,0.0002327652],"domain_scores_gemma":[0.999016,0.0001038897,0.0002014366,0.0005123593,0.0001144714,0.00005178822],"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":[0.000002815948,0.00002673839,0.0002397043,0.00002896381,0.00000722067,5.149548e-7,0.0002178674,0.0007170233,0.1112208,0.6941029,0.0001440646,0.1932914],"study_design_scores_gemma":[0.0005627671,0.00002601537,0.006456984,0.00002021526,0.000009282038,0.00001820573,0.00002050887,0.7783034,0.1864247,0.02725211,0.000720595,0.0001851997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4444798,0.000003576414,0.5539037,0.0003412441,0.0006508048,0.0002413721,0.00000141893,0.000326846,0.00005120784],"genre_scores_gemma":[0.7736944,0.000006361703,0.2257396,0.00003039086,0.0002386735,0.0002209075,0.00005022844,0.000009976075,0.000009441221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7775863,"threshold_uncertainty_score":0.6585758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04911065660645651,"score_gpt":0.2492169924499519,"score_spread":0.2001063358434954,"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."}}