{"id":"W4411867027","doi":"10.1109/tase.2025.3584739","title":"Sequential Image Restoration and Segmentation for Interface Detection in Primary Separation Cells","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image segmentation; Artificial intelligence; Computer vision; Segmentation; Computer science; Separation (statistics); Image restoration; Image (mathematics); Image processing; Pattern recognition (psychology); Machine learning","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":[0.000478694,0.0001028973,0.0001016354,0.0006405757,0.0001608645,0.00015035,0.00003438126,0.00007791354,0.00000146817],"category_scores_gemma":[0.00001148453,0.0001140514,0.00001785203,0.0006341864,0.00003030125,0.0008100142,9.565318e-7,0.0001046779,0.000002052235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003149462,"about_ca_system_score_gemma":0.00003195773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001350314,"about_ca_topic_score_gemma":0.0000172881,"domain_scores_codex":[0.9992792,0.00001211946,0.00023973,0.0001838955,0.0001528533,0.0001321987],"domain_scores_gemma":[0.9997438,0.00004794378,0.00002597031,0.00007465234,0.00007484765,0.00003274326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001266497,0.000005042523,7.632642e-7,0.00006587025,0.000003296971,7.908995e-8,0.0001629766,0.2048208,0.7585759,0.0000104971,0.00001124918,0.03633084],"study_design_scores_gemma":[0.0002780383,0.00003027947,0.0002797362,0.00004181256,0.000005542641,0.000001583236,0.00005673056,0.5138255,0.4853204,0.000007670437,0.00008827397,0.00006437748],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3683679,0.0000138014,0.6303137,0.00001243091,0.0007974138,0.000289186,0.000003017226,0.000139137,0.00006343442],"genre_scores_gemma":[0.998347,0.0000211597,0.001459168,0.00001115883,0.00002173878,0.0001019036,0.000001176024,0.000008703563,0.00002801464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6299791,"threshold_uncertainty_score":0.465088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01215796529679866,"score_gpt":0.2714527770319197,"score_spread":0.2592948117351211,"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."}}