{"id":"W2123033112","doi":"10.1109/tmm.2005.843364","title":"Quality metric for approximating subjective evaluation of 3-D objects","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Computer science; Metric (unit); Quality (philosophy); Artificial intelligence; Perception; Reliability (semiconductor); Texture (cosmology); Graphics; Computer vision; Image quality; Data mining; Pattern recognition (psychology); Image (mathematics); Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006942816,0.0013721,0.0009111358,0.003839785,0.0004303242,0.001679431,0.001583112,0.00134693,0.002192273],"category_scores_gemma":[0.03270603,0.0003786671,0.0009133967,0.002642083,0.001028366,0.002174617,0.001323893,0.0009736745,0.0008238291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001230846,"about_ca_system_score_gemma":0.0006368291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003859616,"about_ca_topic_score_gemma":0.00275314,"domain_scores_codex":[0.9933976,0.002382261,0.0005390109,0.0007081318,0.002814886,0.0001580619],"domain_scores_gemma":[0.9859306,0.00649712,0.001538147,0.001670423,0.004127787,0.0002359411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001041292,0.0004071819,0.01661902,0.00147325,0.0004359903,0.0002923239,0.0008600879,0.4170845,0.1189843,0.03431881,0.006389805,0.4020934],"study_design_scores_gemma":[0.00002600276,0.0003958171,0.006957106,0.00005546498,0.00004519896,0.0002920387,0.00009637821,0.9725204,0.01066624,0.005066082,0.003785449,0.00009385176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01369882,0.0003650734,0.9842777,0.00003578274,0.00004903339,0.0001325372,0.0001982976,0.000411222,0.0008315466],"genre_scores_gemma":[0.3623106,0.0005711917,0.6342189,0.00009925834,0.00009054371,0.0004726764,0.0008134391,0.0002337346,0.001189593],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006942816,"threshold_uncertainty_score":0.03671753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0923963486530244,"score_gpt":0.3920083632978217,"score_spread":0.2996120146447974,"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."}}