{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008610906,0.0004514899,0.0004269571,0.0003784134,0.0009235595,0.0005391347,0.0004569628,0.0001705772,0.000002324378],"category_scores_gemma":[0.000002633134,0.0004599978,0.00008037498,0.001044684,0.00004724906,0.0005760959,0.00003478344,0.000712322,0.00001223189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004468116,"about_ca_system_score_gemma":0.0003557438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007947439,"about_ca_topic_score_gemma":0.0002462614,"domain_scores_codex":[0.9970179,0.0003524028,0.0004368253,0.001007248,0.0006168126,0.0005688085],"domain_scores_gemma":[0.9979224,0.0005696005,0.0002487669,0.0004186307,0.0005096792,0.0003309582],"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":[0.0005543592,0.002589236,0.01825481,0.001181739,0.0007383139,0.00008727687,0.03333922,0.508733,0.001094206,0.001870663,0.0006096441,0.4309475],"study_design_scores_gemma":[0.00177084,0.0007079825,0.008137412,0.000290786,0.00004340127,0.00004011744,0.001148133,0.9855406,0.00131469,0.0002945571,0.0002383952,0.0004731452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2464154,0.0000246497,0.7463134,0.0006018509,0.005031436,0.000389045,0.000001996693,0.0004087653,0.0008134877],"genre_scores_gemma":[0.9765982,0.000004247791,0.02074169,0.001989909,0.0005844062,0.00001923354,0.000004084463,0.00003588875,0.0000223385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7301828,"threshold_uncertainty_score":0.9997852,"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."}}