{"id":"W4322746013","doi":"10.2352/ei.2023.35.8.iqsp-302","title":"Age-specific perceptual image quality assessment","year":2023,"lang":"en","type":"article","venue":"Electronic Imaging","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Faurecia (Canada); McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image quality; Metric (unit); Observer (physics); Artificial intelligence; Perception; Contrast (vision); Computer science; Image (mathematics); Visibility; Computer vision; Image contrast; Quality (philosophy); Quality Score; Pattern recognition (psychology); Psychology; Geography; Engineering","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":[0.001211705,0.0005238928,0.0003624313,0.001004294,0.0001367243,0.0005229673,0.0004163442,0.0004831057,0.001217178],"category_scores_gemma":[0.004875455,0.0001703168,0.000610668,0.0004103992,0.0002190371,0.000878324,0.0005428743,0.000279578,0.0003330482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000358343,"about_ca_system_score_gemma":0.0002479552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002159483,"about_ca_topic_score_gemma":0.002348482,"domain_scores_codex":[0.9995611,0.00009989358,0.0000344888,0.0001233658,0.0001470734,0.00003412817],"domain_scores_gemma":[0.9981164,0.0004253417,0.0003532637,0.0002430851,0.0007699053,0.00009198445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007594132,0.0003825555,0.2886314,0.0004991854,0.0004625498,0.0004264631,0.0006441219,0.1472406,0.1688683,0.004463132,0.002210157,0.3854121],"study_design_scores_gemma":[0.00002506096,0.0009628354,0.2743057,0.00005388895,0.0002079463,0.001245703,0.0002133582,0.6485816,0.06608162,0.00496597,0.003230192,0.0001262192],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4819151,0.0009595449,0.5126581,0.00009608077,0.00004549359,0.000176717,0.0004997072,0.0007736551,0.002875625],"genre_scores_gemma":[0.9323059,0.0003260783,0.06609591,0.00004114599,0.00001663455,0.00006197939,0.0003025964,0.00004908363,0.0008006687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002159483,"threshold_uncertainty_score":0.006408155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04119883847731828,"score_gpt":0.3584759512656984,"score_spread":0.3172771127883801,"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."}}