{"id":"W2612360215","doi":"10.1145/3053381","title":"Statistical Learning of Domain-Specific Quality-of-Service Features from User Reviews","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Internet Technology","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Quality of service; Mobile QoS; Service provider; Service (business); Domain (mathematical analysis); Quality (philosophy); Set (abstract data type); Selection (genetic algorithm); Data mining; World Wide Web; Machine learning; Information retrieval; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002475966,0.000214242,0.0005085845,0.0003205755,0.0001812542,0.0000640321,0.003765379,0.0002437636,0.0001404929],"category_scores_gemma":[0.00003975774,0.0001859435,0.0001144653,0.0003239833,0.0001954235,0.0002140804,0.0001586607,0.0006450547,0.00007248359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002049612,"about_ca_system_score_gemma":0.0000224855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001697438,"about_ca_topic_score_gemma":0.001623439,"domain_scores_codex":[0.9982788,0.0001461661,0.0005784988,0.0005122219,0.0002296065,0.000254695],"domain_scores_gemma":[0.9965584,0.0003730603,0.0004951033,0.002380039,0.0001370878,0.00005635262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002166511,0.0004916094,0.003196432,0.0002614301,0.000289229,0.0000194974,0.005336331,0.0001386233,0.02347283,0.4969173,0.0003726477,0.4692874],"study_design_scores_gemma":[0.002888043,0.001358351,0.0439955,0.00119171,0.0001066784,0.0000540812,0.00172723,0.0005655446,0.2441971,0.2031393,0.4995901,0.001186406],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1149495,0.0005321342,0.8784404,0.004774962,0.0004094523,0.0001851822,0.00002937917,0.0001737566,0.0005052584],"genre_scores_gemma":[0.8387881,0.000259768,0.1603858,0.0003856249,0.00002517038,0.00002610618,0.000008591161,0.00001517265,0.0001056501],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7238386,"threshold_uncertainty_score":0.7582555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0313941700493992,"score_gpt":0.3100884845433601,"score_spread":0.2786943144939609,"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."}}