{"id":"W1997550408","doi":"10.1155/2014/528414","title":"Maintainability Analysis of Underground Mining Equipment Using Genetic Algorithms: Case Studies with an LHD Vehicle","year":2014,"lang":"en","type":"article","venue":"Journal of Mining","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Maintainability; Engineering; Genetic algorithm; Automation; Data mining; Set (abstract data type); Reliability engineering; Computer science; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009354436,0.0001618585,0.0005310434,0.0003972789,0.0001198566,0.00004538076,0.0001121757,0.00004685508,0.000006578053],"category_scores_gemma":[0.0001126141,0.0001304734,0.0001016854,0.0005165882,0.00006493838,0.0002389992,0.00002943041,0.0001459257,9.808597e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001997687,"about_ca_system_score_gemma":0.00004228783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002354428,"about_ca_topic_score_gemma":0.00005976389,"domain_scores_codex":[0.9986714,0.00007984637,0.000598225,0.0001379142,0.0002680633,0.0002445724],"domain_scores_gemma":[0.9989735,0.0002062461,0.0003205634,0.0001621245,0.0002259885,0.0001116076],"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":[0.00003195562,0.00003200852,0.01811215,0.0001871763,0.001556507,0.0004423496,0.006594612,0.951211,0.002255554,0.000006809984,0.000008090479,0.01956177],"study_design_scores_gemma":[0.0006109496,0.0005558761,0.00343712,0.0003185927,0.001668636,0.001381667,0.03368569,0.9571955,0.0008046183,0.00007115531,0.00001618671,0.000254022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703106,0.0006464733,0.02881949,0.00001427893,0.000101583,0.00002854897,0.000001021264,0.00001866356,0.00005931849],"genre_scores_gemma":[0.9271899,0.00001209026,0.07259781,0.000009290674,0.0001609099,7.034848e-7,4.151644e-7,0.00002083823,0.000008074698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04377832,"threshold_uncertainty_score":0.532055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0407513010024291,"score_gpt":0.3057978595195143,"score_spread":0.2650465585170852,"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."}}