{"id":"W2599226259","doi":"10.1002/cav.1749","title":"CODE: Crowd‐optimized design of environments","year":2017,"lang":"en","type":"article","venue":"Computer Animation and Virtual Worlds","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; University of British Columbia; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Modular design; Code (set theory); Crowd simulation; Aggregate (composite); Design flow; Human–computer interaction; Embedded system; Programming language; Crowds","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001071611,0.0000862785,0.0001208547,0.0000443754,0.0001086577,0.00007888479,0.0001212826,0.000039774,0.00005945271],"category_scores_gemma":[0.000006837045,0.00008809246,0.00002292103,0.00002055495,0.00005056089,0.0001776275,0.00004276143,0.00005240646,0.00002228555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001357531,"about_ca_system_score_gemma":0.000003817295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.271227e-7,"about_ca_topic_score_gemma":0.000001047623,"domain_scores_codex":[0.9995556,0.00001743229,0.000163587,0.00009013982,0.00009192011,0.00008132168],"domain_scores_gemma":[0.9996697,0.00002903455,0.00005551843,0.000190873,0.00001050751,0.00004435084],"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":[0.00009816203,0.0001261313,0.0014304,0.0001042406,0.0001675983,0.000004649636,0.001685765,0.7748044,0.01952347,0.01936839,0.008110412,0.1745764],"study_design_scores_gemma":[0.0005988608,0.00004550892,0.0153146,0.00001666897,0.00000846759,0.000001340865,0.00001019414,0.9821499,0.0006359695,0.0001066777,0.00101284,0.00009896088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02879869,0.00003538408,0.9697419,0.00004564211,0.0001749531,0.00009481446,0.000005518105,0.00005545041,0.001047625],"genre_scores_gemma":[0.9753013,0.0000879869,0.02401278,0.00005309548,0.00004078647,0.0000043027,0.000008601629,0.00001292069,0.0004782384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9465026,"threshold_uncertainty_score":0.3592306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02164110309028684,"score_gpt":0.2448238321768636,"score_spread":0.2231827290865768,"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."}}