{"id":"W4404029936","doi":"10.1109/icccnt61001.2024.10724768","title":"“User AR” — Leveraging AI Based Augmented Reality for User Manuals","year":2024,"lang":"en","type":"article","venue":"","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Algoma University","funders":"","keywords":"Augmented reality; Computer science; Human–computer interaction; User interface; World Wide Web; Multimedia; Operating system","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":[0.0005759139,0.0007969987,0.0004947149,0.0009313218,0.0002970044,0.001067645,0.001414903,0.0008289211,0.004429217],"category_scores_gemma":[0.001924266,0.0003313466,0.0006096212,0.000526576,0.0005429074,0.001356591,0.00106767,0.0005920268,0.002287581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002038157,"about_ca_system_score_gemma":0.0003814906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001653752,"about_ca_topic_score_gemma":0.001867933,"domain_scores_codex":[0.998781,0.0001992549,0.00007853798,0.0002450817,0.00062498,0.00007114878],"domain_scores_gemma":[0.9990527,0.00023977,0.0001012469,0.0002928459,0.0002694686,0.00004396194],"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":[0.0003222016,0.0002478599,0.001381328,0.0004817283,0.00006690364,0.0006682345,0.0006527948,0.01252948,0.1126372,0.009491611,0.008536014,0.8529846],"study_design_scores_gemma":[0.0001200955,0.001274047,0.008002877,0.0002161918,0.0001696508,0.005806563,0.0004489222,0.6204123,0.1557347,0.006694809,0.2008169,0.0003028149],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02924296,0.0009340587,0.947829,0.0002153878,0.0001614835,0.0002131228,0.0001001366,0.009092677,0.01221128],"genre_scores_gemma":[0.3236185,0.001036966,0.6546538,0.0003522577,0.000114903,0.0002024321,0.0004292456,0.0004379699,0.01915395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004429217,"threshold_uncertainty_score":0.01481724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03824566748591918,"score_gpt":0.3284753274975477,"score_spread":0.2902296600116285,"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."}}