{"id":"W4367849093","doi":"10.32920/22743668.v1","title":"LayART: Generating indoor layout using ARCore Transformations","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"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; Computer graphics (images); Floor plan; Camera phone; Plan (archaeology); Image (mathematics); Mobile device; Mobile phone; Engineering drawing; Engineering; Geography; Telecommunications","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":[0.0001039794,0.0002265538,0.0002230437,0.0001804916,0.0001007061,0.0001377464,0.0001247216,0.0002514692,0.00005721686],"category_scores_gemma":[0.00001394161,0.0002351682,0.0001021202,0.0001576236,0.00001289204,0.0000704164,0.00005091995,0.0003671092,0.00005306828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009029197,"about_ca_system_score_gemma":0.00004433977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008690687,"about_ca_topic_score_gemma":0.0001109472,"domain_scores_codex":[0.9989913,0.00001927381,0.0003933056,0.0001878563,0.000173024,0.0002353148],"domain_scores_gemma":[0.9995538,0.0000265761,0.00003605715,0.0002568796,0.00005915559,0.0000675581],"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":[3.747536e-7,0.000004773798,0.00009104115,0.0002042165,0.0000372226,0.000003261311,0.0003516155,0.9965656,0.001258201,0.000503812,0.0005346353,0.0004452252],"study_design_scores_gemma":[0.0001037159,0.000003533597,0.00004731245,0.0001016016,0.00003206023,0.000002021109,0.0000771432,0.9974257,0.001394596,0.0003226757,0.0002458606,0.0002438254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09404364,0.00007196376,0.9010718,0.00006441741,0.001032617,0.0002863616,0.00007675893,0.0008492217,0.002503247],"genre_scores_gemma":[0.9566557,0.0001021556,0.04133076,0.00008056651,0.0004302651,0.00002912257,0.0008237613,0.0001520589,0.0003956147],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8626121,"threshold_uncertainty_score":0.958988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06513060119854788,"score_gpt":0.2618870454466143,"score_spread":0.1967564442480664,"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."}}