{"id":"W4249405391","doi":"10.32920/ryerson.14652081","title":"Old tradition, new technologies: comprehension and retention using augmented reality","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Augmented reality; Newspaper; Advertising; Comprehension; Recall; Multimedia; Disadvantage; Computer science; Mode (computer interface); Internet privacy; Psychology; Human–computer interaction; Business; Cognitive psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003155503,0.0003826069,0.0003712022,0.001059297,0.0005325099,0.003009591,0.0006970695,0.0008792098,0.005480114],"category_scores_gemma":[0.02343302,0.0002523926,0.00043897,0.0005177033,0.0009284101,0.003476281,0.001425916,0.0008180856,0.0007953262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004725924,"about_ca_system_score_gemma":0.0004818768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002967247,"about_ca_topic_score_gemma":0.002156227,"domain_scores_codex":[0.9985598,0.0004708302,0.0001105496,0.000205925,0.000511229,0.000141608],"domain_scores_gemma":[0.9856162,0.009079842,0.001803196,0.001129518,0.001911833,0.0004593712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00134877,0.001998591,0.2420277,0.0007048386,0.0001845191,0.001362683,0.4205232,0.0006539342,0.03202815,0.001792083,0.001685942,0.2956896],"study_design_scores_gemma":[0.0001423083,0.008398428,0.6498108,0.000539908,0.0005064089,0.003291978,0.2799757,0.006240456,0.02689585,0.004603196,0.01929509,0.0002998262],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928851,0.0002513138,0.0009861878,0.0001197598,0.000007811193,0.00003268371,0.00003033128,0.00003120496,0.005655685],"genre_scores_gemma":[0.9958034,0.0002820464,0.0007592126,0.00005202399,0.00001260462,0.0000273824,0.00004782051,0.00001676963,0.002998849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005480114,"threshold_uncertainty_score":0.01833278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1030950015755179,"score_gpt":0.3031481781955589,"score_spread":0.200053176620041,"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."}}