{"id":"W1993463692","doi":"10.1007/s10707-013-0187-x","title":"Context-based mobile GeoBI: enhancing business analysis with contextual metrics/statistics and context-based reasoning","year":2013,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Centre de Géomatique du Québec","funders":"","keywords":"Computer science; Context (archaeology); Business intelligence; Data science; Geospatial analysis; Context analysis; Knowledge management; Mobile business development; Context awareness; Mobile device; Mobile technology; World Wide Web; Mobile Web; Geography; Cartography","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":[0.00145736,0.001310437,0.001109317,0.005123379,0.0007267766,0.003066892,0.001094034,0.0009561558,0.002761407],"category_scores_gemma":[0.006498051,0.0005089408,0.0007344277,0.004357729,0.0004478684,0.003807039,0.002090442,0.0008624607,0.001610554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006365505,"about_ca_system_score_gemma":0.0009775921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008760994,"about_ca_topic_score_gemma":0.01933451,"domain_scores_codex":[0.9989294,0.0002990933,0.00008913218,0.0001923187,0.000413521,0.00007650587],"domain_scores_gemma":[0.9978207,0.0009086585,0.0002408379,0.0003747529,0.0005222057,0.0001328174],"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":[0.001007141,0.001096013,0.02744265,0.0008855577,0.0005594557,0.0007505545,0.002013922,0.06418458,0.02855815,0.04412707,0.01507451,0.8143003],"study_design_scores_gemma":[0.00007292057,0.0001987464,0.008525548,0.000177308,0.0002818448,0.0004052675,0.0008596586,0.8806797,0.0229665,0.06128515,0.02442848,0.0001188042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04058135,0.0006863563,0.9401169,0.0004906712,0.0000912649,0.000211471,0.001146842,0.01115052,0.005524585],"genre_scores_gemma":[0.4681022,0.000533447,0.5267158,0.0002331304,0.00009070487,0.0001740029,0.001667271,0.0005118034,0.00197167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008760994,"threshold_uncertainty_score":0.01741999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004341479975383401,"score_gpt":0.1973574144246456,"score_spread":0.1930159344492622,"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."}}