{"id":"W4404294162","doi":"10.2139/ssrn.5018609","title":"Robust to Outlier Image Inpainting for Interface Detection in Primary Separation Vessel","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Fluid Dynamics and Mixing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Inpainting; Outlier; Artificial intelligence; Image (mathematics); Computer vision; Separation (statistics); Pattern recognition (psychology); Computer science; Anomaly detection; Interface (matter); Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001582396,0.0002807252,0.000289696,0.000443394,0.00006704828,0.0002461286,0.0002230175,0.0002270336,0.000002530142],"category_scores_gemma":[0.00004999417,0.0003079726,0.0001519037,0.0001756854,0.000007896519,0.0001096172,0.0001978106,0.003955223,0.00002079111],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004248542,"about_ca_system_score_gemma":0.0005337617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003111446,"about_ca_topic_score_gemma":0.001323186,"domain_scores_codex":[0.9976388,0.00002942604,0.000474772,0.0003087435,0.0001466684,0.001401644],"domain_scores_gemma":[0.9996004,0.00003356256,0.00006829616,0.0001655191,0.00007125814,0.00006104085],"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":[0.00004901376,0.00002042265,0.00001524311,0.0005469676,0.0002137227,0.000004229799,0.0007078153,0.8812643,0.05940164,0.001830815,0.0002010065,0.0557448],"study_design_scores_gemma":[0.0004175779,0.0001408526,0.00009012174,0.0006003252,0.00007643525,0.0001086256,0.00040487,0.9184276,0.002914188,0.07552628,0.0007195587,0.0005735132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3168191,0.002540371,0.6780515,0.0001289854,0.001374577,0.0004072143,0.000007281678,0.0001317441,0.0005392697],"genre_scores_gemma":[0.9949985,0.0009634056,0.002795151,0.00002324627,0.0005301223,0.00008601951,0.00001735912,0.0001271731,0.0004590918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6781793,"threshold_uncertainty_score":0.9999372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007205245837253855,"score_gpt":0.2399378410437479,"score_spread":0.232732595206494,"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."}}