{"id":"W2041218528","doi":"10.1002/qre.1147","title":"FaBSR: a method for cluster failure prediction based on Bayesian serial revision and an application to LANL cluster","year":2010,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Cluster (spacecraft); Failure rate; Bayesian probability; Scale (ratio); Computer science; Series (stratigraphy); Population; Statistics; Reliability engineering; Mathematics; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002283521,0.0007919076,0.001081111,0.001373562,0.0004636541,0.0005014988,0.001611329,0.0007783574,0.001651136],"category_scores_gemma":[0.006110163,0.0003631276,0.0005740947,0.001035102,0.0004246633,0.0007853613,0.0007396921,0.0009375138,0.0006496127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000637378,"about_ca_system_score_gemma":0.001094693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01669861,"about_ca_topic_score_gemma":0.01066594,"domain_scores_codex":[0.9991773,0.0002903353,0.00003762497,0.0001591753,0.0002816206,0.00005403033],"domain_scores_gemma":[0.9977258,0.0009543527,0.0002436504,0.0003009835,0.0006959407,0.00007921691],"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":[0.0001992972,0.00008165476,0.004414036,0.00007401458,0.00009463305,0.000114098,0.0001212772,0.7279876,0.004236981,0.00358275,0.003687673,0.2554061],"study_design_scores_gemma":[0.000005114964,0.000008271126,0.0002517638,0.000001460294,0.000002886308,0.00001002004,0.000002792577,0.998719,0.0003437286,0.0004708458,0.0001789326,0.000005304067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01903666,0.0001112228,0.9777836,0.00008382749,0.00002580529,0.0000507792,0.0001071581,0.002351103,0.0004498069],"genre_scores_gemma":[0.4739503,0.0001836307,0.5227645,0.0001020877,0.00007002342,0.0002113594,0.0005868953,0.0003034511,0.001827697],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01669861,"threshold_uncertainty_score":0.03320283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008304275900135313,"score_gpt":0.3000407202795615,"score_spread":0.2917364443794262,"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."}}