{"id":"W4408033756","doi":"10.21203/rs.3.rs-5596193/v1","title":"AI-SSIM: Human-Centric Image Assessment through Pseudo-Reference Generation and Logical Consistency Analysis in AI-Generated Visuals","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Consistency (knowledge bases); Image (mathematics); Computer science; Artificial intelligence; Computer vision; Computer graphics (images)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001195815,0.0006166571,0.0004877872,0.001490152,0.0003015311,0.001780491,0.001523786,0.0007507263,0.007845171],"category_scores_gemma":[0.007731085,0.0002402059,0.0003691898,0.000704407,0.000513942,0.002144178,0.001741359,0.000672003,0.00178701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005257376,"about_ca_system_score_gemma":0.000607404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002519422,"about_ca_topic_score_gemma":0.002743237,"domain_scores_codex":[0.9992779,0.0002016072,0.0000374509,0.0001679991,0.000262907,0.00005221178],"domain_scores_gemma":[0.9978639,0.0007713516,0.0001709857,0.0004592503,0.0005740284,0.0001604141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001021059,0.000261049,0.003702811,0.0003160628,0.0001085067,0.0002122229,0.0005854126,0.02092056,0.08382761,0.01906002,0.009598181,0.8603865],"study_design_scores_gemma":[0.00007309888,0.0002459126,0.004088267,0.00002570097,0.00003653701,0.0001584529,0.000157258,0.9159583,0.05267321,0.02166823,0.004879265,0.00003579808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06859848,0.0002346296,0.9110485,0.000165289,0.00008993735,0.0002382074,0.0005275623,0.01400239,0.005094992],"genre_scores_gemma":[0.5253465,0.0001080039,0.4694979,0.00007483707,0.00004817421,0.0001483277,0.0008355905,0.001086132,0.002854605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007845171,"threshold_uncertainty_score":0.0262447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1611713441652229,"score_gpt":0.4940134275501158,"score_spread":0.332842083384893,"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."}}