{"id":"W2113472489","doi":"10.1111/j.1467-8640.2009.00348.x","title":"POYRAZ: CONTEXT‐AWARE SERVICE SELECTION UNDER DECEPTION","year":2009,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Service provider; Service (business); Context (archaeology); Selection (genetic algorithm); Service design; Computer science; Deception; Service level objective; Service delivery framework; Business service provider; Consumer behaviour; Marketing; Business; Psychology; Social psychology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001593464,0.0002230495,0.0001652606,0.0001973488,0.0002656968,0.0002134625,0.0009056299,0.0000902676,0.00007285131],"category_scores_gemma":[0.000006728299,0.000218376,0.00007382013,0.001264508,0.00002325607,0.000664021,0.0001044499,0.0002141342,0.0004249119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007517306,"about_ca_system_score_gemma":0.000119342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001070733,"about_ca_topic_score_gemma":0.0002180285,"domain_scores_codex":[0.9982502,0.00007708427,0.0003528187,0.0005431905,0.0004609318,0.0003157012],"domain_scores_gemma":[0.9986722,0.0001925074,0.0001372592,0.0002816339,0.0005804889,0.0001358361],"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":[0.00002130046,0.0001343139,0.0001429872,0.00001870258,0.00002311761,0.000004401261,0.001733603,0.4792189,0.0004125501,0.3309485,0.0002328502,0.1871087],"study_design_scores_gemma":[0.0001324778,0.0001772078,0.01034655,0.00005303858,0.000008783075,0.00007810607,0.0003082186,0.7190676,0.0034798,0.2643383,0.001668408,0.0003415323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01814922,0.0001418657,0.9731033,0.006878743,0.0003366166,0.0001771753,0.000002557164,0.0003772243,0.0008333069],"genre_scores_gemma":[0.9504088,0.000008686627,0.02921407,0.0201263,0.0001423327,0.00000807197,0.00003768356,0.000008841118,0.00004517123],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9438892,"threshold_uncertainty_score":0.8905115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02096013433539515,"score_gpt":0.2781498017645634,"score_spread":0.2571896674291682,"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."}}