{"id":"W2040195773","doi":"10.1142/s0129626406002472","title":"A PARALLEL PROBABILISTIC SYSTEM-LEVEL FAULT DIAGNOSIS APPROACH FOR LARGE MULTIPROCESSOR SYSTEMS","year":2006,"lang":"en","type":"article","venue":"Parallel Processing Letters","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Probabilistic logic; A priori and a posteriori; Multiprocessing; Parallel algorithm; Identification (biology); Fault (geology); Node (physics); Parallel computing; Evolutionary algorithm; Computation; Algorithm; 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":[0.0005060555,0.0004050943,0.0004212573,0.0005278999,0.0004561561,0.00042469,0.0008466512,0.0007081804,0.001054],"category_scores_gemma":[0.001568023,0.000238501,0.0004762504,0.0003462736,0.0005219755,0.0006859874,0.0004962991,0.000600912,0.0002005596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006426135,"about_ca_system_score_gemma":0.0008414452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002488616,"about_ca_topic_score_gemma":0.002763515,"domain_scores_codex":[0.9996612,0.00006615924,0.00001434205,0.0000696956,0.0001606985,0.00002805243],"domain_scores_gemma":[0.9995579,0.0002144481,0.00005183012,0.00006221019,0.00009596047,0.00001764672],"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":[0.00004537028,0.00004020534,0.000998548,0.00005175803,0.00003362036,0.0001380575,0.0000487732,0.8715819,0.009075021,0.01282222,0.0003462782,0.1048183],"study_design_scores_gemma":[0.000007920936,0.00001867443,0.0001553932,0.000002624447,0.000008324456,0.00007959923,0.000004650203,0.9902603,0.001882066,0.006928321,0.0006480834,0.000003949242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01157706,0.00007867838,0.9870712,0.000111216,0.00001017549,0.00002385038,0.00001044501,0.0003056528,0.0008116587],"genre_scores_gemma":[0.4081593,0.0001497531,0.5897423,0.00009142297,0.00002091111,0.0000891299,0.00005508247,0.00004854465,0.001643549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002488616,"threshold_uncertainty_score":0.004948258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03660718432135544,"score_gpt":0.2467189114810148,"score_spread":0.2101117271596594,"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."}}