{"id":"W4399906363","doi":"10.18280/ria.380314","title":"Optimized Cathode Protection Model for Best Anode Parameter Selection Using Machine Learning Approach: Iraq—Case Study","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Model selection; Anode; Computer science; Cathode; Artificial intelligence; Machine learning; Engineering; Physics; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004604321,0.0008761061,0.0006836759,0.0006327879,0.0004969233,0.001090684,0.0008494189,0.001655405,0.003262555],"category_scores_gemma":[0.0007177452,0.0003334307,0.000774926,0.0004490662,0.000337276,0.0003467948,0.0004284532,0.0005964132,0.0003077284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042899,"about_ca_system_score_gemma":0.00114369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02305019,"about_ca_topic_score_gemma":0.0159754,"domain_scores_codex":[0.9998822,0.00003069729,0.00000723659,0.0000251719,0.00002553462,0.0000291592],"domain_scores_gemma":[0.9996628,0.0001815241,0.00003471797,0.00001217695,0.00009638318,0.00001247255],"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":[0.00002641325,0.00002954963,0.0006391965,0.00006581035,0.00001165281,0.0001843004,0.00002319656,0.9924057,0.0008247658,0.0005184048,0.0002967489,0.004974195],"study_design_scores_gemma":[0.000006133574,0.00003892906,0.0002606165,0.000006323798,0.000008943724,0.00002199047,0.00002439661,0.9984781,0.0006032111,0.0001955134,0.0003517816,0.00000418639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6030238,0.00164246,0.3561907,0.001005777,0.00009866183,0.000328472,0.0007812467,0.0007986196,0.03613029],"genre_scores_gemma":[0.967122,0.0002536764,0.02767297,0.000044574,0.000008365045,0.0001294368,0.0002102361,0.00002176073,0.004537047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02305019,"threshold_uncertainty_score":0.04583204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1354795146210189,"score_gpt":0.3217422614751391,"score_spread":0.1862627468541202,"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."}}