{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003604183,0.0004244133,0.0005084299,0.00007433449,0.0002844431,0.0006425909,0.003299644,0.0003862158,0.0007546758],"category_scores_gemma":[0.000068478,0.0004044817,0.000295827,0.0006133792,0.0002185297,0.0006258457,0.005721364,0.0007236595,0.00008808768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002643407,"about_ca_system_score_gemma":0.0005532827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001904857,"about_ca_topic_score_gemma":0.0001478007,"domain_scores_codex":[0.9961152,0.0002327661,0.0006656731,0.001756107,0.0007380673,0.0004921537],"domain_scores_gemma":[0.9957198,0.0001155883,0.0004256598,0.003218649,0.0003510547,0.0001692531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002645772,0.001852814,0.0001881628,0.0006533819,0.001336429,0.0002311419,0.2277055,0.002745156,0.002254356,0.6257201,0.1075857,0.02970087],"study_design_scores_gemma":[0.002720125,0.0003324526,0.003641976,0.001587032,0.000447477,0.0002328591,0.1882857,0.3374322,0.1764211,0.1392682,0.1409821,0.008648916],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004302181,0.0004403652,0.9363105,0.01141173,0.0007885172,0.0006735071,0.00002545788,0.0004100504,0.04563767],"genre_scores_gemma":[0.8695604,0.0006852585,0.1174438,0.003359551,0.0002698081,0.002446909,0.0007194995,0.00004874607,0.005466044],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8652582,"threshold_uncertainty_score":0.9998407,"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."}}