{"id":"W2508006725","doi":"10.1177/1541931213601458","title":"Assessment of Technical Quality of Online Video Using Visualization in Place of Experience","year":2016,"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":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Telus (Canada); University of Toronto","funders":"","keywords":"Schematic; Computer science; Subjective video quality; Video quality; Visualization; Quality (philosophy); Multimedia; Artificial intelligence; Image quality; 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":[0.001153826,0.000131319,0.0003957641,0.00004813786,0.00008278677,0.00001773167,0.0005395657,0.00008538728,0.000001108525],"category_scores_gemma":[0.000175467,0.00008850095,0.0001529745,0.0002308592,0.0002681458,0.0005744456,0.0005623769,0.00009011943,8.157299e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001110182,"about_ca_system_score_gemma":0.00007566536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000304188,"about_ca_topic_score_gemma":0.00001567418,"domain_scores_codex":[0.9983922,0.00003227179,0.000885782,0.0002556353,0.0002657139,0.0001684462],"domain_scores_gemma":[0.9982215,0.0001991159,0.001101527,0.0001480053,0.0002976616,0.00003214932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001065988,0.0001944978,0.3119797,0.0003059671,0.00001805136,1.086469e-8,0.009668839,0.00004473981,0.6552656,0.02236208,0.00001130224,0.0001386087],"study_design_scores_gemma":[0.0007123551,0.0001578423,0.6617817,0.0009148949,0.00001749496,7.266699e-7,0.01316448,0.005194566,0.3168157,0.0009910973,0.000009507556,0.0002396797],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945421,0.0000242948,0.0051193,0.00005371265,0.00003804692,0.0001375866,0.00002203215,0.0000109437,0.00005201897],"genre_scores_gemma":[0.9847967,0.00003156014,0.01512786,0.00001534235,0.00001315928,0.000002141722,5.824842e-7,0.000006698913,0.000005976302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.349802,"threshold_uncertainty_score":0.3608964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04626450347073867,"score_gpt":0.3585416642106835,"score_spread":0.3122771607399448,"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."}}