{"id":"W4367673526","doi":"10.32920/22734395.v1","title":"Efficient and Scalable Object Localization in 3D on Mobile Device","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":"Natural Sciences and Engineering Research Council of Canada; Ontario Centre of Innovation","keywords":"Leverage (statistics); Computer science; Mobile device; Artificial intelligence; Object (grammar); Augmented reality; Computer vision; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002060155,0.0008419846,0.0005808948,0.0006223109,0.0003101941,0.0008394719,0.001121503,0.0006686434,0.003897017],"category_scores_gemma":[0.0007177021,0.0005139426,0.0006727955,0.0006653056,0.0004079338,0.001248902,0.001987291,0.0005906603,0.002501101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004380542,"about_ca_system_score_gemma":0.0004790318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006472959,"about_ca_topic_score_gemma":0.009829062,"domain_scores_codex":[0.9996281,0.00003239503,0.00001185738,0.0001061943,0.0001720703,0.00004933682],"domain_scores_gemma":[0.9997281,0.00005036977,0.00002472368,0.0001051129,0.00007304699,0.00001858481],"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":[0.0003126973,0.0001349894,0.002290558,0.0003040914,0.0001473851,0.0006311316,0.0002799864,0.1693241,0.1098793,0.01300206,0.02710468,0.6765891],"study_design_scores_gemma":[0.00002477599,0.00007879162,0.001160267,0.00002426024,0.00001814237,0.0002497165,0.0000768287,0.9592825,0.01806647,0.005879417,0.01510632,0.00003260546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02261411,0.0004460962,0.963823,0.0001899938,0.0001448692,0.00005710727,0.000322047,0.007424681,0.004978134],"genre_scores_gemma":[0.4016894,0.0005824563,0.5855069,0.0002621393,0.0001024379,0.000173928,0.001419806,0.0004395852,0.009823407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006472959,"threshold_uncertainty_score":0.01303679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02005682424580987,"score_gpt":0.2407883141720535,"score_spread":0.2207314899262436,"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."}}