{"id":"W2044931474","doi":"10.1145/1151454.1151467","title":"A context-aware mobile service discovery and selection mechanism using artificial neural networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Mechanism (biology); Artificial neural network; Context (archaeology); Selection (genetic algorithm); Service discovery; Service (business); Artificial intelligence; Mobile computing; Computer network; World Wide Web; Business; Web service; Geography","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":[0.0001967261,0.0001912841,0.0001636597,0.00008772998,0.0002339585,0.0005230598,0.0003037679,0.00006608586,0.000021797],"category_scores_gemma":[0.000001328294,0.0001815825,0.00004552959,0.000576131,0.00001886068,0.0008567888,0.0003272535,0.0001219202,0.000006172665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000963768,"about_ca_system_score_gemma":0.00002507516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001430523,"about_ca_topic_score_gemma":0.0016501,"domain_scores_codex":[0.998521,0.00008518443,0.00026977,0.0005163776,0.0002196175,0.0003880794],"domain_scores_gemma":[0.9994736,0.00004919829,0.00009916324,0.0002539775,0.00006180161,0.00006226076],"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":[0.00001902748,0.00009973369,0.0003343183,0.00002502634,0.00002448462,0.00002558335,0.0000491726,0.7643272,0.001197144,0.205982,0.0008617041,0.02705467],"study_design_scores_gemma":[0.0001755714,0.00007008418,0.0002199053,0.00001041467,0.00001497567,0.00001587713,0.00004483139,0.9943187,0.0008493554,0.003820665,0.0002508863,0.0002087703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1982455,0.00006697154,0.8004897,0.0001517459,0.0002996193,0.0004048197,7.251414e-7,0.0002068074,0.0001341046],"genre_scores_gemma":[0.9941224,0.000003834168,0.003947731,0.001368474,0.0003059848,0.00005489576,0.00000528651,0.00001536981,0.000175959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7965419,"threshold_uncertainty_score":0.7404719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424431043654193,"score_gpt":0.2206605591344596,"score_spread":0.2064162486979177,"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."}}