{"id":"W4251350931","doi":"10.32920/ryerson.14652081.v1","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002618768,0.0002828648,0.0003487974,0.0001847366,0.0002212745,0.0004304964,0.0008296819,0.0004529555,0.00001687532],"category_scores_gemma":[0.00004344047,0.0002885307,0.0001112739,0.0004747196,0.0001159542,0.0003276613,0.002420954,0.0005658221,0.000003946838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002319476,"about_ca_system_score_gemma":0.0002832595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001452764,"about_ca_topic_score_gemma":0.00006844896,"domain_scores_codex":[0.997665,0.0001149427,0.0005004693,0.001119616,0.0003586736,0.0002412601],"domain_scores_gemma":[0.9975625,0.00005748816,0.000316431,0.001779523,0.0001750407,0.0001090218],"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":[0.00002446833,0.001097376,0.0004473737,0.001446335,0.0007180572,0.00007887997,0.002275176,0.0143629,0.07337881,0.6669877,0.03000261,0.2091803],"study_design_scores_gemma":[0.0006171395,0.00004199829,0.003156143,0.00069893,0.0001173807,0.0001153363,0.0007559467,0.8558408,0.01025617,0.1259402,0.001618466,0.0008414564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02099568,0.0003324033,0.9684106,0.00736599,0.0002720593,0.0006148562,0.00001634605,0.001405663,0.0005863646],"genre_scores_gemma":[0.6564703,0.0006593143,0.3414574,0.0003626953,0.00008029952,0.00007568031,0.0004677333,0.00002947311,0.0003971572],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8414779,"threshold_uncertainty_score":0.9999567,"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."}}