{"id":"W4254097878","doi":"10.32920/ryerson.14662218.v1","title":"Opportunities to utilize the potential of Augmented Reality to interact with photographic material in museum and archive collections","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":"Concordia University; Toronto Metropolitan University","funders":"","keywords":"Augmented reality; Storytelling; Computer science; Object (grammar); Visual arts; Key (lock); Multimedia; Digital storytelling; Photography; World Wide Web; Human–computer interaction; Art; Narrative; Artificial intelligence; Literature","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":[],"consensus_categories":[],"category_scores_codex":[0.0003607323,0.0002354629,0.000352212,0.0004775509,0.0001632361,0.000389221,0.0008870102,0.00008596104,0.00005689395],"category_scores_gemma":[0.00002637511,0.0001754698,0.00007353815,0.0007555146,0.0001544196,0.0000997977,0.002404447,0.0003126622,7.627537e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006778959,"about_ca_system_score_gemma":0.0003690135,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01060065,"about_ca_topic_score_gemma":0.01139601,"domain_scores_codex":[0.9980375,0.0002753593,0.0004808304,0.0006555524,0.0003083537,0.0002423789],"domain_scores_gemma":[0.9982424,0.0001053826,0.0001729228,0.001147753,0.0001712955,0.0001601926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.006069033,0.01333305,0.003832388,0.004388887,0.007496716,0.001032505,0.1958815,0.1327094,0.2112476,0.247058,0.1065232,0.07042762],"study_design_scores_gemma":[0.008576335,0.00430669,0.3485458,0.008411067,0.001208449,0.001529901,0.09426364,0.3345753,0.1038182,0.04865607,0.03743406,0.008674483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1935733,0.000008354441,0.7910933,0.01008868,0.0003155924,0.001839591,0.0003277523,0.0001008569,0.002652553],"genre_scores_gemma":[0.9835333,0.00007119578,0.01466061,0.0005419949,0.00002796498,0.0007329384,0.0001036861,0.0000144284,0.0003138454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.78996,"threshold_uncertainty_score":0.9959878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03871547141328837,"score_gpt":0.272327407263866,"score_spread":0.2336119358505776,"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."}}