{"id":"W2742453911","doi":"10.1145/3098954.3098979","title":"Using Markov Chains to Model Sensor Network Reliability","year":2017,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Reliability (semiconductor); Reliability engineering; Context (archaeology); Markov chain; Markov model; Distributed computing; Component (thermodynamics); Markov process; Network topology; Fault tolerance; Maintenance engineering; Engineering; Machine learning; Computer network","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":[0.002510864,0.001056479,0.000793289,0.001085501,0.0005192292,0.001170912,0.001331335,0.001361029,0.001840334],"category_scores_gemma":[0.007370038,0.0007274165,0.0009121182,0.0008557515,0.001277998,0.001604299,0.0008328616,0.001562125,0.0003591137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001734608,"about_ca_system_score_gemma":0.001066168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01815697,"about_ca_topic_score_gemma":0.01383753,"domain_scores_codex":[0.9988716,0.0005538049,0.000049214,0.0001636532,0.0002213048,0.0001403278],"domain_scores_gemma":[0.9944781,0.004407884,0.0004874757,0.0002006281,0.000324935,0.0001009168],"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":[0.00001737064,0.000008776672,0.0004370142,0.000009875373,0.000009696674,0.00002357968,0.00002398394,0.9869297,0.0001620383,0.01078416,0.0001197244,0.001474226],"study_design_scores_gemma":[0.000004051264,0.000007114111,0.00007260228,0.000003085763,0.000003655673,0.00000547282,0.000003766055,0.9919488,0.00007843241,0.007755211,0.0001138327,0.000003897464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04866634,0.0007079965,0.9453725,0.0005729611,0.00007784211,0.00009604247,0.0003406651,0.0003759336,0.003789766],"genre_scores_gemma":[0.9365317,0.001448618,0.05514279,0.0001430862,0.0001110622,0.0004015363,0.0005133537,0.00007331288,0.005634509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01815697,"threshold_uncertainty_score":0.03610259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04921105564984695,"score_gpt":0.3113396137913693,"score_spread":0.2621285581415224,"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."}}