{"id":"W2069228602","doi":"10.1109/crv.2013.51","title":"A Markerless Augmented Reality System for Mobile Devices","year":2013,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"USable; Orb (optics); Computer science; Augmented reality; Pose; Virtual reality; Mobile device; Range (aeronautics); Computer vision; Artificial intelligence; Real-time computing; Image (mathematics); Multimedia; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005469931,0.001392989,0.001025124,0.001037823,0.0006463603,0.001712011,0.001971936,0.001125078,0.02683057],"category_scores_gemma":[0.001522011,0.0007309145,0.0007472382,0.000877881,0.0002687602,0.00132643,0.002486062,0.001101008,0.01438766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000309783,"about_ca_system_score_gemma":0.0005745523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009623226,"about_ca_topic_score_gemma":0.0011965,"domain_scores_codex":[0.9990849,0.000125582,0.00008274193,0.0001944774,0.0004489324,0.0000633184],"domain_scores_gemma":[0.999283,0.00006953774,0.00006708867,0.0003112761,0.0002036992,0.00006541106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008759209,0.0001905282,0.00156838,0.0006945611,0.0002117472,0.0006981989,0.000490666,0.004751208,0.1779778,0.0087973,0.08175147,0.7219922],"study_design_scores_gemma":[0.000426833,0.001423264,0.008417748,0.0002742695,0.0003706761,0.004503101,0.0001836204,0.1187745,0.1733513,0.005616458,0.6861612,0.0004969859],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01086782,0.0008132859,0.9129069,0.0001855272,0.0004030454,0.0004199964,0.002752383,0.05870368,0.01294752],"genre_scores_gemma":[0.2637908,0.001259627,0.6869663,0.0005059997,0.0002408826,0.001032752,0.008257021,0.002571981,0.03537464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02683057,"threshold_uncertainty_score":0.08975726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0111750330494426,"score_gpt":0.2146110735372178,"score_spread":0.2034360404877752,"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."}}