{"id":"W4413680157","doi":"10.1109/compsac65507.2025.00180","title":"Robust Smartphone Screen Integration with Deep Learning for Virtual Reality Pass-through","year":2025,"lang":"en","type":"article","venue":"","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Virtual reality; Computer science; Human–computer interaction; Augmented reality; Deep learning; Multimedia; Computer graphics (images); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002401634,0.001071945,0.000523121,0.0005338793,0.0002167351,0.0008103931,0.001065158,0.0005191161,0.004304351],"category_scores_gemma":[0.00114708,0.0003932586,0.0004867504,0.0002409997,0.000209268,0.0009344714,0.001497046,0.0006438267,0.001763163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004739478,"about_ca_system_score_gemma":0.0004055667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004522685,"about_ca_topic_score_gemma":0.009117789,"domain_scores_codex":[0.9996194,0.00004139307,0.00001325652,0.0001055864,0.000152757,0.00006760395],"domain_scores_gemma":[0.9997314,0.00005866398,0.00002506606,0.00006595656,0.00009284598,0.00002618002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006435861,0.0003426422,0.003379883,0.0002029174,0.0001737229,0.0004637832,0.0002376065,0.03759825,0.1840879,0.001733163,0.009016681,0.7621198],"study_design_scores_gemma":[0.00003230147,0.0002515004,0.00342451,0.0000250045,0.00007017604,0.0004184414,0.0000772284,0.8992841,0.08726845,0.001635808,0.007468922,0.00004364324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07539322,0.0003886525,0.9078073,0.0001707992,0.0001347391,0.0001018895,0.0003240319,0.01233274,0.003346656],"genre_scores_gemma":[0.7337186,0.0003148306,0.2581482,0.0002585348,0.00004436604,0.0001256648,0.0007884697,0.0005391391,0.006062199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004522685,"threshold_uncertainty_score":0.01439947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04962168726827117,"score_gpt":0.3177226082374408,"score_spread":0.2681009209691696,"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."}}