{"id":"W2952967990","doi":"10.48550/arxiv.1803.06248","title":"3D Video Quality Metric for Mobile Applications","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Metric (unit); Computer science; Distortion (music); Quality (philosophy); Computer vision; Video quality; Artificial intelligence; Subjective video quality; Mobile device; Image quality; Image (mathematics); Engineering; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009034143,0.0003072863,0.0004119445,0.0003663134,0.0003039126,0.0002197441,0.002478412,0.0002772032,0.00003676952],"category_scores_gemma":[0.00006006351,0.0003596552,0.0003335683,0.00102973,0.0001333276,0.0004407035,0.002028063,0.0003346346,0.0001857679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003039929,"about_ca_system_score_gemma":0.0003461386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001946009,"about_ca_topic_score_gemma":0.00002936446,"domain_scores_codex":[0.9974508,0.0002263148,0.000356655,0.001411456,0.000139033,0.0004156886],"domain_scores_gemma":[0.9962919,0.0004278032,0.0004137424,0.00220203,0.0004875533,0.000176949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006396266,0.0009675578,0.001305685,0.0009369324,0.0004347519,0.00003435273,0.0006602326,0.03875807,0.0000837265,0.9295257,0.007387008,0.01984197],"study_design_scores_gemma":[0.001717342,0.0003864777,0.001908462,0.0001000175,0.0003092668,0.000004081047,0.0002866303,0.5408757,0.001310502,0.3210188,0.1300885,0.001994221],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005848717,0.0000982346,0.9892532,0.0001020636,0.0004028908,0.001512312,0.00007407714,0.000312662,0.002395864],"genre_scores_gemma":[0.9539223,0.00009326689,0.04259312,0.0003369112,0.000281908,0.0000981479,0.00006416431,0.00002259944,0.002587629],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9480735,"threshold_uncertainty_score":0.9998856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1344460294673856,"score_gpt":0.2820002448775724,"score_spread":0.1475542154101868,"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."}}