{"id":"W2078852715","doi":"10.1109/tsc.2015.2413111","title":"CCCloud: Context-Aware and Credible Cloud Service Selection Based on Subjective Assessment and Objective Assessment","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Wollongong; University of Victoria","keywords":"Cloud computing; Computer science; Context (archaeology); Collusion; Credibility; Cloud testing; Data mining; Selection (genetic algorithm); Benchmark (surveying); Machine learning; Cloud computing security","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003662163,0.001344135,0.001205005,0.002045399,0.0009957341,0.002971031,0.002007496,0.00108499,0.001116058],"category_scores_gemma":[0.01302104,0.0005230259,0.0009633344,0.001453465,0.0009889515,0.004072419,0.002505144,0.001626663,0.0002600706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002419931,"about_ca_system_score_gemma":0.002565179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01168722,"about_ca_topic_score_gemma":0.01083471,"domain_scores_codex":[0.9944554,0.001845815,0.0003546966,0.0009778591,0.00199692,0.0003692588],"domain_scores_gemma":[0.9935024,0.002783944,0.001051196,0.0005581401,0.001599198,0.0005049789],"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":[0.0008741577,0.0006977992,0.03752362,0.0005671869,0.0004869682,0.001055817,0.001608049,0.5969468,0.01122098,0.0481093,0.005783864,0.2951254],"study_design_scores_gemma":[0.00002018754,0.00008983357,0.002437833,0.00002896357,0.00006483447,0.0001066518,0.0001070119,0.986853,0.001035523,0.007852593,0.001349367,0.00005422892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07692055,0.0008874121,0.911971,0.0008261323,0.0001065972,0.0004940432,0.0002095488,0.0009451441,0.007639611],"genre_scores_gemma":[0.9280609,0.0002199377,0.06995264,0.0001440886,0.00006471794,0.0001855967,0.0001572509,0.00003987133,0.00117503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01168722,"threshold_uncertainty_score":0.02323836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02555015777364206,"score_gpt":0.2922483581638534,"score_spread":0.2666982003902113,"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."}}