{"id":"W2017275588","doi":"10.1504/ijmme.2009.029320","title":"GenRel: A computerised model for reliability prediction of mining machinery","year":2009,"lang":"en","type":"article","venue":"International Journal of Mining and Mineral Engineering","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Reliability engineering; Computer science; Engineering; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002829983,0.0001396648,0.0002386307,0.0002283541,0.00002193803,0.00003459722,0.0001506001,0.00006222301,0.000001453586],"category_scores_gemma":[0.000127009,0.0001328354,0.00009492956,0.00005856765,0.00001193791,0.0002140061,0.00001423434,0.0001151373,5.557992e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005191153,"about_ca_system_score_gemma":0.00002259832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001223277,"about_ca_topic_score_gemma":3.548261e-7,"domain_scores_codex":[0.9990434,0.000005202777,0.0005134077,0.00009899286,0.0001949863,0.0001440192],"domain_scores_gemma":[0.9994611,0.00008642725,0.0001285687,0.00005850871,0.0001924845,0.00007286893],"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":[0.00004795871,0.00002059086,0.0003344409,0.0000693449,0.00006697817,0.000005217357,0.00106777,0.9366083,0.05080719,0.00004260566,0.0005744743,0.01035511],"study_design_scores_gemma":[0.0007320942,0.0001125968,0.001412935,0.0002957614,0.0000241892,0.00009524707,0.00004966792,0.9963656,0.0006367794,0.00004839632,0.000121625,0.0001050965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7824631,0.0003078617,0.2164435,0.00006961107,0.0005492793,0.00003498335,0.00001771349,0.00004733383,0.00006662852],"genre_scores_gemma":[0.9261168,0.00003601052,0.07337201,0.00002029569,0.0003807946,0.000001705547,0.0000064843,0.00001709788,0.00004884896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1436537,"threshold_uncertainty_score":0.541687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130361454749475,"score_gpt":0.2339735718412456,"score_spread":0.2209374263662981,"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."}}