{"id":"W3094186564","doi":"10.1109/bigmm50055.2020.00047","title":"LayART: Generating indoor layout using ARCore Transformations","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Science and Engineering Research Board; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer vision; RGB color model; Artificial intelligence; Floor plan; Computer graphics (images); Plan (archaeology); Camera phone; Mobile phone; Mobile device; Image (mathematics); Engineering drawing; Engineering","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":[0.00002260699,0.00007430579,0.00007596143,0.00002582517,0.00006259628,0.00004006282,0.0000402754,0.0000371608,0.00009063933],"category_scores_gemma":[0.00000795565,0.00007235222,0.00002892906,0.0001413364,0.000006119351,0.0001085583,0.000003897638,0.00006714866,0.00002405576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001578818,"about_ca_system_score_gemma":0.000008869957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006546092,"about_ca_topic_score_gemma":0.000007307636,"domain_scores_codex":[0.9995804,0.000007239491,0.000157004,0.00006484699,0.0000755748,0.0001149638],"domain_scores_gemma":[0.9998396,0.000008365588,0.000008673938,0.0000533261,0.00002060647,0.00006944063],"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":[4.901756e-7,0.000002391929,0.0001304023,0.00002484814,0.000007091109,0.000001071701,0.0005505962,0.9792618,0.0182908,0.00081442,0.0002779718,0.000638097],"study_design_scores_gemma":[0.0001326686,0.000008069604,0.00002036008,0.000005656407,0.000007646517,0.000001416129,0.0001002444,0.9905316,0.008218406,0.00001281645,0.000875179,0.00008589438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1676317,0.00003262052,0.8279508,0.0001844155,0.00006759683,0.00007639069,0.000006576488,0.0002057863,0.00384411],"genre_scores_gemma":[0.9807771,0.000006379098,0.01860272,0.0004328727,0.0001162631,0.00000155344,0.00002946953,0.00001976244,0.00001388898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8131453,"threshold_uncertainty_score":0.2950438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03460819347748425,"score_gpt":0.2118596479358707,"score_spread":0.1772514544583864,"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."}}