{"id":"W2947362700","doi":"10.1002/sdtp.12843","title":"3‐4: Stereoscopic Image Quality Assessment","year":2019,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Qualcomm (Canada); York University","funders":"Ontario Centres of Excellence","keywords":"Stereoscopy; Image quality; Computer science; Computer vision; Quality (philosophy); Artificial intelligence; Geology; Image (mathematics); Philosophy; Epistemology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008899615,0.0002553053,0.0005116598,0.0001006245,0.00007316233,0.0001362879,0.001455718,0.0001444059,0.0001131345],"category_scores_gemma":[0.00004213948,0.000227216,0.0002406889,0.0003447754,0.0001389028,0.000643764,0.0006305483,0.0003312425,0.0001299926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001806955,"about_ca_system_score_gemma":0.000217241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008992292,"about_ca_topic_score_gemma":0.0000142338,"domain_scores_codex":[0.9971167,0.0002352626,0.0007499799,0.0006555147,0.0007870878,0.0004554971],"domain_scores_gemma":[0.9975533,0.0003005707,0.0002949459,0.001580145,0.0001181985,0.0001528305],"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":[0.000008149731,0.0002564886,0.005677257,0.0001189608,0.00002233745,0.000005702206,0.00006698037,0.00001125577,0.9279462,0.06549475,0.0001617798,0.0002301246],"study_design_scores_gemma":[0.003680384,0.002270604,0.6768044,0.0003673329,0.00007341183,0.00003644869,0.0003754776,0.0001806925,0.2888676,0.002676345,0.02264638,0.002020844],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0735654,0.00005170255,0.003278456,0.00693917,0.0006145092,0.0007484791,0.00001269038,0.0004626419,0.914327],"genre_scores_gemma":[0.9883997,0.00002767448,0.009894856,0.0007127063,0.00003500583,0.00003051166,0.000007378271,0.00001667529,0.0008754842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9148343,"threshold_uncertainty_score":0.9265602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01715466027986392,"score_gpt":0.3228706819492851,"score_spread":0.3057160216694212,"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."}}