{"id":"W4407009884","doi":"10.2139/ssrn.5119212","title":"Smart Casting Process Defects Detection System Framework","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Process (computing); Casting; Computer science; Process engineering; Manufacturing engineering; Engineering; Materials science; Metallurgy; Operating system","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":[0.00217091,0.0004988579,0.0006148354,0.0005294779,0.0004095471,0.0002738196,0.0003553853,0.001186964,0.000005769781],"category_scores_gemma":[0.0002030051,0.0004962183,0.0003613649,0.0004816813,0.00001544265,0.0001298876,0.00009934396,0.01106695,0.00003569194],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004449143,"about_ca_system_score_gemma":0.001651462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001180055,"about_ca_topic_score_gemma":0.000347503,"domain_scores_codex":[0.9959545,0.0001788707,0.0007977991,0.0004164246,0.0004681732,0.002184286],"domain_scores_gemma":[0.9988123,0.0001184135,0.0003339229,0.0003732163,0.0002415836,0.0001205493],"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":[0.0004246834,0.0001031267,0.00121992,0.009104691,0.003772356,0.0001028935,0.001753094,0.64288,0.002732769,0.01782804,0.0002709191,0.3198076],"study_design_scores_gemma":[0.007536542,0.002771334,0.0005408662,0.05390202,0.003483214,0.01925504,0.03992829,0.4005765,0.06308841,0.3867518,0.01133074,0.01083521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3545092,0.005678094,0.6177881,0.0000141041,0.01465257,0.0008122856,0.00001491741,0.00126615,0.005264543],"genre_scores_gemma":[0.9966685,0.0004198499,0.00007051855,0.000006727279,0.002361659,0.00007430843,0.00000438513,0.00007664051,0.0003174187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6421593,"threshold_uncertainty_score":0.9997489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01052354669721779,"score_gpt":0.2445249015705786,"score_spread":0.2340013548733608,"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."}}