{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006519198,0.0002870014,0.0002706303,0.0002728326,0.0002621985,0.0001761357,0.0001396026,0.0001234525,0.00001190983],"category_scores_gemma":[0.0001888753,0.0003036358,0.0001075479,0.0005788399,0.00004186104,0.000273319,0.00004396307,0.0005187042,0.0000144875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002491698,"about_ca_system_score_gemma":0.00002887379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002798016,"about_ca_topic_score_gemma":0.00002056449,"domain_scores_codex":[0.9984621,0.00008311485,0.0004549341,0.0005166439,0.0001217157,0.0003615248],"domain_scores_gemma":[0.9993554,0.0001659248,0.00005521985,0.0002580752,0.0001013712,0.00006405256],"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":[0.00001975208,0.0001158866,0.00008215582,0.0002797331,0.00004117811,0.00003720537,0.00206244,0.9633349,0.02879961,0.0002053085,0.000005610631,0.005016192],"study_design_scores_gemma":[0.00004880689,0.0002133578,2.65592e-7,0.0001121212,0.00008171955,0.0009278425,0.0007238474,0.9690455,0.02489934,0.003618847,0.00001431543,0.0003140311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3116096,0.0001550868,0.685412,0.000005172247,0.000128264,0.00121908,0.000005599773,0.001259716,0.000205464],"genre_scores_gemma":[0.5717335,0.000007859784,0.4277347,0.000001875052,0.00005598825,0.0003063836,0.000004546486,0.00007409688,0.00008108946],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2601238,"threshold_uncertainty_score":0.9999416,"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."}}