{"id":"W4238775359","doi":"10.32920/ryerson.14661306.v1","title":"SHARP: Immersive retail experiences through augmented reality","year":2021,"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":"","keywords":"Augmented reality; Space (punctuation); Object (grammar); Process (computing); Tracking (education); Business; Software; Advertising; Retail sales; Computer science; Marketing; Multimedia; Human–computer interaction; Psychology; Artificial intelligence","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":[0.0004626208,0.0008462205,0.0002820873,0.0004340781,0.000371785,0.002834277,0.0009287162,0.0008396115,0.02825553],"category_scores_gemma":[0.001156743,0.000440151,0.0006183986,0.0003373449,0.0008591572,0.002467324,0.004842382,0.001075446,0.003096492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001834827,"about_ca_system_score_gemma":0.0002181909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003697343,"about_ca_topic_score_gemma":0.0008148332,"domain_scores_codex":[0.999492,0.0001508052,0.00001783763,0.00006171571,0.0002190774,0.00005847045],"domain_scores_gemma":[0.9995927,0.000163122,0.00002004986,0.00009983911,0.00006181558,0.00006242471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001138365,0.0005899888,0.0014596,0.001835628,0.0001532945,0.002346877,0.01667956,0.009561781,0.1495299,0.1156907,0.09151612,0.6094982],"study_design_scores_gemma":[0.0004031347,0.001745505,0.008624466,0.0006897181,0.0002278124,0.007399765,0.006512845,0.0451732,0.05476129,0.05039127,0.8236544,0.0004166923],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1160797,0.0034193,0.661613,0.001628637,0.0009653803,0.0005477088,0.00133625,0.01009846,0.2043116],"genre_scores_gemma":[0.601241,0.002953137,0.2992808,0.0006570066,0.0002650371,0.0004320335,0.001300407,0.0009777548,0.09289279],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02825553,"threshold_uncertainty_score":0.09452415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07509054116408748,"score_gpt":0.3201570752420604,"score_spread":0.2450665340779729,"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."}}