{"id":"W2991252122","doi":"10.1177/1071181319631135","title":"Memories of Video: Impact of Sequencing on Rated Technical Quality for Viewed and Visualized Disruptions","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Telus (Canada); University of Toronto","funders":"","keywords":"Quality (philosophy); Visualization; Quality of experience; Computer science; Subjective video quality; Affect (linguistics); Video quality; Multimedia; Psychology; Image quality; Artificial intelligence; Quality of service; Communication; Telecommunications; Engineering","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":[0.001180362,0.0001732899,0.0004686413,0.00003539387,0.0002149753,0.00006746349,0.0003701859,0.00009270533,0.000001253811],"category_scores_gemma":[0.0001936246,0.0001203593,0.000333861,0.0001542096,0.0001899026,0.0004532283,0.000333775,0.0001353023,6.490884e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009939966,"about_ca_system_score_gemma":0.0000671672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001886732,"about_ca_topic_score_gemma":0.00000310008,"domain_scores_codex":[0.998735,0.0000226623,0.0005845993,0.000289512,0.0001634399,0.0002047802],"domain_scores_gemma":[0.9984404,0.0003188331,0.0007102787,0.000150114,0.0003298576,0.00005054424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001361886,0.0001646193,0.08797726,0.001318246,0.0002334782,1.580793e-8,0.02853076,0.00008408321,0.8229464,0.05805666,0.0002535667,0.0002986818],"study_design_scores_gemma":[0.003186452,0.002017658,0.4072845,0.001308252,0.0001663596,0.000003983282,0.03250199,0.009335513,0.5318967,0.0112896,0.00007746788,0.0009315503],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99879,0.00004445632,0.0005085215,0.0000664687,0.00005123817,0.0003743618,0.00005090577,0.00002449368,0.00008953867],"genre_scores_gemma":[0.9951961,0.00002747925,0.004686554,0.00002975237,0.00001849951,0.000007306825,0.000002260439,0.00000978142,0.00002228795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3193073,"threshold_uncertainty_score":0.490811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0382969086914761,"score_gpt":0.3529415451315579,"score_spread":0.3146446364400818,"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."}}