{"id":"W2094410685","doi":"10.4018/jiit.2006040102","title":"A Database Service Discovery Model for Mobile Agents","year":2006,"lang":"en","type":"article","venue":"International Journal of Intelligent Information Technologies","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Service discovery; Mobile agent; Service (business); Mobile service; Mobile computing; Scope (computer science); Mobile database; Mobile device; Domain (mathematical analysis); World Wide Web; Distributed computing; Computer network; Web service; Mobile station","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002902335,0.0007242343,0.001123711,0.001540415,0.001554978,0.006933104,0.004028676,0.003162088,0.003620854],"category_scores_gemma":[0.004426503,0.0006927183,0.001465982,0.002639291,0.001555835,0.008432922,0.002666664,0.002289349,0.003058003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002511282,"about_ca_system_score_gemma":0.003057067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007347823,"about_ca_topic_score_gemma":0.004593384,"domain_scores_codex":[0.9970846,0.0005759803,0.0003926545,0.0005311751,0.001145736,0.0002699947],"domain_scores_gemma":[0.9980641,0.0005550842,0.0001540281,0.0004788076,0.0005388956,0.0002091153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000132758,0.00008874671,0.0003805853,0.000177185,0.0000418186,0.0003857639,0.0004690817,0.02787619,0.002685194,0.9116844,0.006971711,0.04910649],"study_design_scores_gemma":[0.0001495869,0.0001565516,0.00018105,0.00009183452,0.00009394536,0.001016359,0.0002049143,0.4903848,0.003787452,0.2911124,0.2127289,0.000092241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005171758,0.001212889,0.9676714,0.001735992,0.0001860992,0.0002656047,0.0002781685,0.001430964,0.0220471],"genre_scores_gemma":[0.25965,0.003571932,0.6817741,0.001263142,0.0003967258,0.001242517,0.001481022,0.0003190229,0.05030154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007347823,"threshold_uncertainty_score":0.01822066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01956696074598623,"score_gpt":0.2690136334730966,"score_spread":0.2494466727271104,"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."}}