{"id":"W2296703396","doi":"10.1145/2890103","title":"Depth Personalization and Streaming of Stereoscopic Sports Videos","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Stereoscopy; Personalization; Context (archaeology); Process (computing); Multimedia; Depth perception; Computer vision; Depth of field; Mobile device; Observer (physics); Human–computer interaction; Artificial intelligence; Perception; World Wide Web","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":[0.0002939841,0.0005033402,0.0003705893,0.0006581512,0.0001710244,0.0004685301,0.0006969612,0.0003582149,0.003202147],"category_scores_gemma":[0.001430225,0.0002596123,0.0002650917,0.0003252321,0.0002000453,0.0005630149,0.0007151769,0.0003857182,0.0006424987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003270148,"about_ca_system_score_gemma":0.0002458073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001377477,"about_ca_topic_score_gemma":0.001958463,"domain_scores_codex":[0.9997223,0.00004138478,0.00001380986,0.00006752534,0.0001223656,0.00003256744],"domain_scores_gemma":[0.9995322,0.0001017729,0.0000612292,0.0001080634,0.0001378541,0.00005880105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009539039,0.0001870688,0.002431339,0.0002321723,0.00004318639,0.0003467332,0.0005104801,0.007813903,0.6201075,0.001099796,0.00316519,0.3631088],"study_design_scores_gemma":[0.0002059856,0.00090263,0.02193346,0.00005862229,0.0001077533,0.001706654,0.0003118897,0.3686007,0.5863914,0.002877725,0.0167526,0.0001506077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4228939,0.0004546694,0.560283,0.0002036473,0.00008827324,0.0005474608,0.0007232699,0.007654034,0.007151786],"genre_scores_gemma":[0.719455,0.00038737,0.2741577,0.0001178313,0.00009684148,0.0001471253,0.0005558305,0.000523282,0.004559077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003202147,"threshold_uncertainty_score":0.01071221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03135320210346418,"score_gpt":0.3153955078874255,"score_spread":0.2840423057839613,"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."}}