{"id":"W4284884302","doi":"10.3390/jimaging8070188","title":"Efficient and Scalable Object Localization in 3D on Mobile Device","year":2022,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Augmented reality; Mobile device; Leverage (statistics); Object (grammar); Object detection; Minimum bounding box; Convolutional neural network; Scalability; Pose; Pattern recognition (psychology); Image (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":[0.0002576089,0.00005584522,0.00009491207,0.0001951562,0.00005989351,0.00002640058,0.00004303918,0.000008497947,0.00002573259],"category_scores_gemma":[0.00001654671,0.00005575617,0.00001820168,0.0002114107,0.000007506339,0.00003785545,0.00001449824,0.0001492575,0.000001010943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001350164,"about_ca_system_score_gemma":0.0000146274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006300174,"about_ca_topic_score_gemma":0.000001348253,"domain_scores_codex":[0.9994338,0.00003193176,0.0002138566,0.00005184508,0.0001712521,0.00009734394],"domain_scores_gemma":[0.9998055,0.00003126278,0.000051553,0.00004649657,0.00003439189,0.00003078366],"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":[0.000007393881,0.00002389883,0.003338116,0.00001632974,0.000003507337,0.00002375781,0.0002034662,0.9921845,0.0008573668,0.00002604083,0.0001957173,0.003119926],"study_design_scores_gemma":[0.0003084764,0.00004184286,0.0005653875,0.00003868593,0.000005782625,0.0000440736,0.0002540428,0.9966403,0.0005296745,0.00001792243,0.00149492,0.00005888509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8854051,0.001144868,0.1120889,0.00009387494,0.0004003138,0.00009893863,0.000001616391,0.00002416919,0.0007421848],"genre_scores_gemma":[0.9992582,0.00002690151,0.0005309922,0.0001293719,0.00003157629,0.000001760502,0.00000120842,0.00001370112,0.000006274711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1138531,"threshold_uncertainty_score":0.2273671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005015111700132593,"score_gpt":0.2083085249919523,"score_spread":0.2032934132918198,"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."}}