{"id":"W2118448117","doi":"10.1109/tsc.2010.44","title":"An Adaptive and Intelligent SLA Negotiation System for Web Services","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Negotiation; Service-level agreement; Quality of service; Function (biology); Web service; Service level; Service (business); Service provider; Process management; World Wide Web; Computer network; Business; Marketing","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":[0.001345649,0.0003389722,0.0005068076,0.0004409105,0.00095783,0.001166018,0.001400038,0.0008058712,0.002839927],"category_scores_gemma":[0.002897029,0.0002786836,0.0003060449,0.0003943938,0.0005583538,0.001946221,0.001149134,0.0009234472,0.0008988004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007801611,"about_ca_system_score_gemma":0.001297301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003275832,"about_ca_topic_score_gemma":0.002150239,"domain_scores_codex":[0.9994111,0.0001632297,0.00005675848,0.0001032763,0.0002070419,0.00005859046],"domain_scores_gemma":[0.9993753,0.0001820693,0.00006816421,0.0001071598,0.0001685498,0.0000987766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001971199,0.0009101448,0.002992509,0.0002711292,0.0001535708,0.001042399,0.001067968,0.1702111,0.1279946,0.06650896,0.02001498,0.6068614],"study_design_scores_gemma":[0.0001020271,0.000112787,0.0002890755,0.00001046677,0.00002753708,0.0001435946,0.00005768061,0.9704524,0.01260407,0.00696467,0.009201886,0.00003382684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03491498,0.0001325412,0.9520022,0.0002483907,0.00006935751,0.0002753553,0.00004639196,0.008246119,0.004064642],"genre_scores_gemma":[0.6328375,0.0001373005,0.3618236,0.000138657,0.00005356217,0.0002747615,0.0002312266,0.0002643758,0.004239067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003275832,"threshold_uncertainty_score":0.009500504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01553146166447645,"score_gpt":0.2549581396855782,"score_spread":0.2394266780211017,"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."}}