{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002268289,0.0008907108,0.0006182315,0.000653269,0.0003148883,0.0009241664,0.001192135,0.0006974306,0.004124376],"category_scores_gemma":[0.0007617518,0.0005768302,0.0007110911,0.0006627123,0.0004053989,0.001327434,0.00204911,0.0006320049,0.002566701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004507983,"about_ca_system_score_gemma":0.0004780623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00673193,"about_ca_topic_score_gemma":0.0102903,"domain_scores_codex":[0.9996031,0.00003443359,0.00001231447,0.0001127592,0.0001846377,0.00005285759],"domain_scores_gemma":[0.9997023,0.00005762835,0.00002653169,0.000115034,0.00007834504,0.00002017343],"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":[0.000327119,0.0001444262,0.002284839,0.0002989571,0.000158189,0.0006357887,0.0002891916,0.1426907,0.1120461,0.01095443,0.02618638,0.703984],"study_design_scores_gemma":[0.00002512454,0.00008368117,0.001237092,0.00002431914,0.00001981391,0.0002804577,0.00008189782,0.9597811,0.0190572,0.005310349,0.01406523,0.00003366235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02591264,0.0004942533,0.959348,0.0002065635,0.0001444287,0.00006265163,0.000368666,0.008338823,0.005123937],"genre_scores_gemma":[0.3854649,0.0005883469,0.6023282,0.0002672671,0.00009847407,0.0001611422,0.001398733,0.0004323622,0.009260583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00673193,"threshold_uncertainty_score":0.0137974,"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."}}