{"id":"W2024062765","doi":"10.1109/prdc.2013.33","title":"Derivation of Stochastic Reward Net for Compatibility and Conformance Verification of Component Erroneous Behavior Model","year":2013,"lang":"en","type":"article","venue":"","topic":"Software Reliability and Analysis Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Center for Evolutionary Biology and Medicine, University of Pittsburgh; Albaha University","keywords":"Compatibility (geochemistry); Computer science; Component (thermodynamics); Unified Modeling Language; Conformance testing; Model checking; Behavioral modeling; Reliability engineering; Distributed computing; Theoretical computer science; Programming language; Artificial intelligence; Engineering; Software","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":[0.001884139,0.0006252007,0.0005663894,0.001340102,0.0005436849,0.0009522389,0.0008480089,0.0007227589,0.002336614],"category_scores_gemma":[0.005971858,0.0004127113,0.001491103,0.0004999304,0.000831325,0.0007808335,0.0009086871,0.001036375,0.0003985835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001283616,"about_ca_system_score_gemma":0.002519655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00791082,"about_ca_topic_score_gemma":0.006727802,"domain_scores_codex":[0.9982594,0.0003502971,0.0001273874,0.000260504,0.0008604436,0.000142015],"domain_scores_gemma":[0.9980187,0.0009496193,0.0002723238,0.0001735942,0.0005316543,0.00005401422],"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":[0.0001406484,0.0001137939,0.002641187,0.0001660738,0.00004890736,0.001108311,0.000219183,0.7786644,0.01481759,0.1522071,0.0007283018,0.04914451],"study_design_scores_gemma":[0.00001324131,0.00003630231,0.0002030142,0.00001839224,0.00001699739,0.00008581951,0.00001181767,0.9723059,0.004504802,0.02155623,0.001235482,0.00001202805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01395755,0.00002195061,0.9833266,0.00005106916,0.00002362557,0.0001089865,0.00008909356,0.0004169845,0.002004102],"genre_scores_gemma":[0.4368531,0.0001253036,0.557462,0.00006993232,0.00002266748,0.0005196756,0.000476711,0.0002413786,0.004229288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00791082,"threshold_uncertainty_score":0.01572955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02997162104423442,"score_gpt":0.2823924052743948,"score_spread":0.2524207842301604,"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."}}