{"id":"W2111345321","doi":"10.1109/aina.2005.295","title":"Reliability Estimation of Mobile Agent Systems Using the Monte Carlo Approach","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Monte Carlo method; Robustness (evolution); Reliability (semiconductor); Reliability theory; Mobile agent; Cellular network; Distributed computing; Mathematical optimization; Reliability engineering; Mathematics; Engineering; Computer network; Failure rate","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002065118,0.0008000432,0.0011889,0.001547734,0.0005284361,0.0009567555,0.0009846358,0.0009061313,0.0008967773],"category_scores_gemma":[0.01159107,0.0005731513,0.0007347395,0.0005991203,0.0009841176,0.001423039,0.0008087199,0.001078303,0.0002646988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009290308,"about_ca_system_score_gemma":0.001027262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003434293,"about_ca_topic_score_gemma":0.001784064,"domain_scores_codex":[0.9985315,0.0006902434,0.00006288823,0.0001780672,0.0004328296,0.0001045209],"domain_scores_gemma":[0.9933115,0.004933168,0.0005646904,0.0004666226,0.0006129803,0.0001109332],"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":[0.00006932417,0.00001690503,0.0009322877,0.00003228187,0.00004508215,0.00004991647,0.00004163929,0.960201,0.001263,0.01423889,0.0002209898,0.0228886],"study_design_scores_gemma":[0.000003832522,0.000008918548,0.0001140621,0.000004141573,0.000004095646,0.00001895479,0.000003187138,0.993521,0.000558399,0.005567803,0.0001880986,0.000007492108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008093906,0.0001820047,0.9910981,0.00005717084,0.0000104827,0.00001483515,0.000009042747,0.0001856567,0.0003488106],"genre_scores_gemma":[0.7121614,0.0005558377,0.2858677,0.00006275842,0.00007073743,0.0001344116,0.0001125245,0.00009269635,0.000941915],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003434293,"threshold_uncertainty_score":0.01092154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01970651993342224,"score_gpt":0.2696999875027362,"score_spread":0.249993467569314,"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."}}