{"id":"W4412511289","doi":"10.1149/ma2025-01261465mtgabs","title":"Use of Accelerated Corrosion Techniques to Aid in the Selection of Ground Support in Underground Mines","year":2025,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Corrosion; Selection (genetic algorithm); Underground mining (soft rock); Environmental science; Groundwater; Mining engineering; Engineering; Waste management; Computer science; Metallurgy; Materials science; Geotechnical engineering; Coal mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007590387,0.0001540411,0.0002582669,0.0004478292,0.00002520424,0.00003659116,0.0002031,0.0001156034,0.000002576031],"category_scores_gemma":[0.0008942589,0.0001411749,0.0000272096,0.001185664,0.00003434921,0.0001941646,0.00003895738,0.0002158843,6.092009e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001366527,"about_ca_system_score_gemma":0.00004284328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001234697,"about_ca_topic_score_gemma":0.0007106065,"domain_scores_codex":[0.9988356,0.00007064682,0.0005680816,0.0001678287,0.0001550575,0.0002027341],"domain_scores_gemma":[0.9989911,0.0005496385,0.0001150858,0.0001960374,0.0001288574,0.00001931183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002926674,0.00008498382,0.04286904,0.0008135987,0.000007093332,0.000006872734,0.0003815863,0.005118744,0.9497835,0.0001559656,0.0005837638,0.0001655822],"study_design_scores_gemma":[0.0001205295,0.00009437097,0.1657923,0.002230898,0.00001345467,0.000007761414,0.0001042651,0.0003632681,0.8274662,0.003588387,0.0000596096,0.0001589684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861154,0.00001599209,0.0002066169,0.00004765279,0.0000748216,0.0004614698,0.000002618978,0.000297439,0.01277801],"genre_scores_gemma":[0.944948,0.00001090749,0.05491696,0.0000330746,0.00001199514,0.0000404326,0.000005001522,0.00002020882,0.00001345061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1229233,"threshold_uncertainty_score":0.5756947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03640606672794471,"score_gpt":0.2838277619948644,"score_spread":0.2474216952669197,"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."}}