{"id":"W4367673189","doi":"10.32920/22734395","title":"Efficient and Scalable Object Localization in 3D on Mobile Device","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centre of Innovation","keywords":"Leverage (statistics); Computer science; Mobile device; Augmented reality; Computer vision; Object (grammar); Artificial intelligence; Scalability; Convolutional neural network; Object detection; Dimension (graph theory); Pattern recognition (psychology); Mathematics","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":[0.0003981429,0.0001597088,0.0001768091,0.0002347742,0.00007858456,0.0001793774,0.0006256248,0.000162451,0.00001259588],"category_scores_gemma":[0.0000271135,0.0001523548,0.00002818852,0.0006043146,0.00003784867,0.00008781755,0.001497427,0.0002736808,0.0001995073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001591212,"about_ca_system_score_gemma":0.0001113063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007612079,"about_ca_topic_score_gemma":0.0005029484,"domain_scores_codex":[0.9984275,0.00006864225,0.0002754069,0.0007438169,0.0002758584,0.0002088305],"domain_scores_gemma":[0.9987908,0.0001127709,0.00009421686,0.0008737391,0.00005865348,0.00006982386],"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":[0.000004075056,0.0003345858,0.001190966,0.00026334,0.00002824528,0.000009613243,0.001228849,0.8923967,0.00005731833,0.06856481,0.007261797,0.02865965],"study_design_scores_gemma":[0.0001292093,0.00003188553,0.001627094,0.000129731,0.000004559441,0.000001444449,0.00004204334,0.9862685,0.0004618886,0.006562004,0.004539066,0.0002025705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003645928,0.00005819096,0.9888695,0.0009740384,0.0002218565,0.0009014004,0.000008891041,0.0004003282,0.004919903],"genre_scores_gemma":[0.944026,0.0006519371,0.043701,0.002618034,0.0001168726,0.002787336,0.000230137,0.0000742123,0.005794491],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9451684,"threshold_uncertainty_score":0.6212847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03383090308262855,"score_gpt":0.2977580763419765,"score_spread":0.263927173259348,"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."}}