{"id":"W2013765499","doi":"10.4018/jebr.2005070103","title":"On Personalizing Web Services Using Context","year":2005,"lang":"en","type":"article","venue":"International Journal of E-Business Research","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"World Wide Web; Computer science; Personalization; Web service; Schedule; Context (archaeology); Web development; WS-Policy; Web modeling; Service (business); Web navigation; Web application security; Business; Operating system","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":[0.001154271,0.000159325,0.0002119767,0.001116398,0.0001881071,0.0005509773,0.003147941,0.00007147186,0.0001193933],"category_scores_gemma":[0.00005493338,0.000127464,0.0001137743,0.0009060645,0.00006330716,0.001277883,0.0004792208,0.0005605373,0.00008260043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002148877,"about_ca_system_score_gemma":0.0003794154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002151534,"about_ca_topic_score_gemma":0.0001630524,"domain_scores_codex":[0.9963092,0.0001944116,0.0004758645,0.000274673,0.002366988,0.0003788833],"domain_scores_gemma":[0.9947375,0.000427339,0.0002766246,0.0002892091,0.004112991,0.0001563507],"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":[0.002145223,0.002492063,0.009637933,0.0003898868,0.001566124,0.002333473,0.02885466,0.05665375,0.1271133,0.3130095,0.003198666,0.4526054],"study_design_scores_gemma":[0.008910009,0.0008819112,0.01796327,0.004685881,0.00005225811,0.004005268,0.005171069,0.537892,0.02435675,0.0257518,0.3689082,0.001421593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9526154,0.001012192,0.02083125,0.01945267,0.001748213,0.0001224257,0.000007064935,0.00003920694,0.004171606],"genre_scores_gemma":[0.9892038,0.00008919374,0.006997216,0.002136789,0.001478014,0.000001789403,0.000002118218,0.00001551102,0.00007552939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4812382,"threshold_uncertainty_score":0.584971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05739420329115698,"score_gpt":0.3742458603839841,"score_spread":0.3168516570928271,"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."}}