{"id":"W4246399249","doi":"10.32920/ryerson.14663616","title":"Personalized decision making for QOS-based web service selection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Ranking (information retrieval); Computer science; Quality of service; Selection (genetic algorithm); Service (business); Rank (graph theory); Process (computing); Quality (philosophy); Order (exchange); Web service; Machine learning; Learning to rank; Artificial intelligence; Data mining; World Wide Web; Computer network; Mathematics; Business","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":[0.00314963,0.0008780864,0.001148106,0.001053748,0.0009421002,0.00166199,0.001180926,0.001103952,0.002487044],"category_scores_gemma":[0.008110937,0.0004386101,0.0008701684,0.00126175,0.0006493339,0.001994162,0.0009367124,0.001784882,0.0009268823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001693823,"about_ca_system_score_gemma":0.001715069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006951576,"about_ca_topic_score_gemma":0.008044451,"domain_scores_codex":[0.9962983,0.00162257,0.0001879825,0.0005517052,0.001019279,0.0003202097],"domain_scores_gemma":[0.996494,0.002000585,0.0003239178,0.000412028,0.0005269441,0.0002425368],"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":[0.0003545874,0.0004693629,0.003790333,0.0001371793,0.0001311759,0.000202695,0.0002968067,0.7028889,0.006359546,0.02575244,0.00465701,0.25496],"study_design_scores_gemma":[0.00001372575,0.00003980294,0.0002682116,0.00000364134,0.00001107951,0.00002296824,0.00002314364,0.9891817,0.0007924145,0.009040231,0.0005922469,0.00001069076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05402457,0.0003953163,0.9400553,0.0005508795,0.00006929602,0.0001898196,0.0001308824,0.001085649,0.003498264],"genre_scores_gemma":[0.8052909,0.0002945258,0.1910521,0.0001526105,0.00008795955,0.0001448899,0.000319786,0.00009723226,0.002559898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006951576,"threshold_uncertainty_score":0.01665705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01954047448499455,"score_gpt":0.2903582751382944,"score_spread":0.2708178006532999,"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."}}