{"id":"W2801461733","doi":"10.5539/cis.v11n2p88","title":"A Synergy of Semantic and Context Awareness for Service Composition in Ubiquitous Environment","year":2018,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Task (project management); Context (archaeology); Architecture; Service composition; Semantic Web; Service (business); Composition (language); World Wide Web; Service-oriented architecture; Ubiquitous computing; Perspective (graphical); Ambient intelligence; Context awareness; The Internet; Web service; Human–computer interaction; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003341385,0.0000911758,0.0001278891,0.0002744407,0.0001808153,0.0001600251,0.0004440481,0.00002768053,9.560829e-7],"category_scores_gemma":[0.000002163756,0.00007912193,0.00001239099,0.0005043259,0.0001702906,0.003142586,0.0003506492,0.00003544983,0.000004091475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001533087,"about_ca_system_score_gemma":0.00005100064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001031104,"about_ca_topic_score_gemma":0.00003236226,"domain_scores_codex":[0.9991205,0.00001579759,0.0002734223,0.0001966912,0.0002149414,0.0001786155],"domain_scores_gemma":[0.9993603,0.00006477651,0.0001169844,0.000216484,0.0001730926,0.00006835167],"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":[0.00008241134,0.0001172016,0.00574827,0.0007186592,0.00001629165,0.000001094525,0.0671773,0.001288792,0.006534421,0.1487761,0.00004395465,0.7694955],"study_design_scores_gemma":[0.0007645208,0.0002920049,0.04945636,0.0001047398,0.000003621228,0.0000267103,0.0002461648,0.9348634,0.009638986,0.001281872,0.003149344,0.0001722481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4477308,0.00003111681,0.5513322,0.0004348543,0.0001538524,0.0001582924,0.000002660185,0.00001890544,0.0001373327],"genre_scores_gemma":[0.9791008,0.00002065722,0.01827841,0.00254997,0.00003357846,0.000009890606,0.000004631828,0.000001540083,4.759604e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9335746,"threshold_uncertainty_score":0.3226499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00855877365720191,"score_gpt":0.2288940970404632,"score_spread":0.2203353233832613,"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."}}