{"id":"W3096388188","doi":"10.1145/3385959.3422703","title":"BUDI: Building Urban Designs Interactively Can Spatial-Based Collaboration be Seamless?","year":2020,"lang":"en","type":"article","venue":"Symposium on Spatial User Interaction","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Visualization; Server; Human–computer interaction; Space (punctuation); Virtual reality; Quality (philosophy); Data visualization; Multimedia; World Wide Web; Artificial intelligence; Operating system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001716645,0.0003059503,0.0002732118,0.0002480554,0.0002248659,0.000810555,0.0006259718,0.0001083658,0.0001042504],"category_scores_gemma":[0.0001947845,0.0003155222,0.0001034503,0.000755572,0.00003210472,0.001280925,0.0001389624,0.0003043077,0.0001029846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002993428,"about_ca_system_score_gemma":0.0002347029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007615966,"about_ca_topic_score_gemma":0.0005231481,"domain_scores_codex":[0.9976724,0.0002580779,0.0004915199,0.0007202628,0.0005579304,0.0002997698],"domain_scores_gemma":[0.9983456,0.0001752952,0.0003961342,0.0004427107,0.0003850209,0.0002552507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001930858,0.00200868,0.00703266,0.000316062,0.0004604719,0.0001288826,0.02066418,0.07857535,0.695321,0.06836211,0.09200121,0.03319857],"study_design_scores_gemma":[0.0008472753,0.000705298,0.0003173801,0.0001024565,0.00003493591,0.000002597058,0.0002214727,0.7193624,0.2162751,0.00004523247,0.06162868,0.0004572031],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01212683,0.000002703308,0.960598,0.02431527,0.001428417,0.0003508347,0.00007132938,0.0003417349,0.0007648759],"genre_scores_gemma":[0.9865,0.000006514484,0.003481976,0.009181095,0.0004611096,0.00002892636,0.0001932816,0.0000357444,0.0001113785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9743732,"threshold_uncertainty_score":0.9999297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0451630005803328,"score_gpt":0.3210009909631144,"score_spread":0.2758379903827816,"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."}}