{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004385932,0.0002314199,0.0002280961,0.0002052419,0.0005663069,0.0003579384,0.000535755,0.0001385148,0.000003480902],"category_scores_gemma":[4.870028e-7,0.000224502,0.00008110073,0.0002671804,0.0000157791,0.0008470614,0.000006626909,0.0002191889,0.00002058715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004567681,"about_ca_system_score_gemma":0.00003057579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003229989,"about_ca_topic_score_gemma":0.001162704,"domain_scores_codex":[0.9983748,0.00009255405,0.0004092294,0.0005824686,0.0002521607,0.0002887639],"domain_scores_gemma":[0.9987592,0.0001741852,0.0002511284,0.0004797234,0.0001860648,0.0001496686],"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":[0.0002751486,0.001076579,0.001286722,0.004784261,0.00039309,0.00001110796,0.04207557,0.1450648,0.3167482,0.02278613,0.00001465376,0.4654837],"study_design_scores_gemma":[0.0004235301,0.0001872722,0.0007599377,0.000167517,0.00002314927,0.00001822582,0.00111308,0.9810824,0.01579351,0.0000391301,0.0001604596,0.000231722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3622548,0.00001505652,0.6353389,0.00005057091,0.001607002,0.0003917273,0.00001086533,0.0002839593,0.00004708452],"genre_scores_gemma":[0.9716167,0.000003440584,0.02796921,0.0001457343,0.0001976626,0.00002892925,0.000005933271,0.00002124946,0.00001107567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8360177,"threshold_uncertainty_score":0.9154927,"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."}}