{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001542383,0.0001974682,0.0002131313,0.0002471877,0.00003010784,0.00007831021,0.00007882866,0.0002298636,0.0000380288],"category_scores_gemma":[0.00002379362,0.0001988212,0.0000274482,0.0002553818,0.00001507789,0.00002973608,0.00008879774,0.000231969,0.00007319935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001335052,"about_ca_system_score_gemma":0.00002161384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001980347,"about_ca_topic_score_gemma":0.0002795687,"domain_scores_codex":[0.9990293,0.00002637204,0.0002666404,0.0003091909,0.0001711082,0.0001973877],"domain_scores_gemma":[0.999581,0.00005192274,0.00002581142,0.0002477562,0.00003852008,0.00005499033],"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":[0.000002568943,0.00001682986,0.0007523925,0.0002721899,0.000009534778,0.000004801324,0.0001024271,0.9965659,0.00004036096,0.0002642115,0.0009044835,0.001064304],"study_design_scores_gemma":[0.0001494028,0.00002562802,0.0006913078,0.0002140884,0.000008795584,4.424067e-7,0.00004195328,0.9965349,0.0007430163,0.0002483404,0.001124112,0.0002179903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2605585,0.0006564244,0.7187272,0.0001177276,0.002381593,0.001839708,0.00003876471,0.001721699,0.01395846],"genre_scores_gemma":[0.9969808,0.0006427547,0.001224967,0.0001269757,0.00006539221,0.00008404399,0.0002228872,0.00008697914,0.0005651313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7364224,"threshold_uncertainty_score":0.8107695,"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."}}