{"id":"W2993126184","doi":"10.1109/bigmm.2019.00023","title":"Novel Segmentation Metrics for Use in Augmented Reality Advertisement Integration","year":2019,"lang":"en","type":"article","venue":"","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer science; Facade; Augmented reality; Artificial intelligence; Sample (material); Machine learning; Computer vision; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001858882,0.001232826,0.0008049265,0.004380802,0.0004642998,0.003148844,0.001047179,0.001273094,0.001861432],"category_scores_gemma":[0.01128873,0.0004351255,0.0005533672,0.002785874,0.0007414027,0.00262188,0.001515569,0.0008433074,0.0005508268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001239791,"about_ca_system_score_gemma":0.0008144264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003306524,"about_ca_topic_score_gemma":0.004542149,"domain_scores_codex":[0.9980502,0.00030005,0.0002313141,0.0003185915,0.0009464269,0.000153388],"domain_scores_gemma":[0.9958001,0.001456091,0.00074972,0.0004316195,0.001377154,0.0001852867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001104887,0.0004093564,0.02517686,0.001044238,0.0002691654,0.000319648,0.00102234,0.1615398,0.09727515,0.01777492,0.006215017,0.6878486],"study_design_scores_gemma":[0.0000535347,0.0004649675,0.01908209,0.0001184325,0.0001467032,0.0007380476,0.0004628489,0.8897882,0.07180455,0.008073619,0.009136345,0.0001306074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.14814,0.00200466,0.8366866,0.0002357842,0.0001360731,0.0003408773,0.001044571,0.004344114,0.007067309],"genre_scores_gemma":[0.5702345,0.000432787,0.4258341,0.0001089459,0.00005104275,0.0001872937,0.001648159,0.0005393192,0.0009638364],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004380802,"threshold_uncertainty_score":0.009830773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06276593101471527,"score_gpt":0.3160487438160715,"score_spread":0.2532828128013562,"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."}}