{"id":"W2186003804","doi":"","title":"Using Ontology Based Knowledge Discovery in Location Based Services","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Ontology; Computer science; Location-based service; Context (archaeology); Domain (mathematical analysis); Process (computing); Domain knowledge; Data science; Service (business); Raw data; Knowledge extraction; Data mining; Service provider; World Wide Web; Artificial intelligence; 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":[0.0002798667,0.00007386577,0.00007771185,0.0001496027,0.00003433041,0.0001264354,0.0004523499,0.00002635211,0.00001875413],"category_scores_gemma":[0.000004327667,0.00006441744,0.00001606665,0.0004197107,0.00001222155,0.002503135,0.0001433571,0.00003738409,0.00005785441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003763894,"about_ca_system_score_gemma":0.00003595985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002472922,"about_ca_topic_score_gemma":0.0003922344,"domain_scores_codex":[0.9993501,0.00005136746,0.0001167082,0.0001718551,0.00007985725,0.0002300953],"domain_scores_gemma":[0.9995447,0.00004076881,0.00003448337,0.000322976,0.00002087055,0.00003619966],"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":[0.00003306219,0.002927379,0.3967265,0.0008674781,0.0000408913,0.00002062193,0.001718621,0.008227531,0.001577739,0.4044702,0.001596336,0.1817936],"study_design_scores_gemma":[0.0002707165,0.000009449427,0.0220832,0.00002123455,0.000002984489,2.918894e-7,0.00002588129,0.9744653,0.0005556546,0.0001098517,0.002352228,0.0001032882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01552488,0.0001021803,0.9786409,0.00025393,0.0003719017,0.00008490404,7.481165e-7,0.00006200525,0.004958595],"genre_scores_gemma":[0.9173883,5.160547e-7,0.08172304,0.000577783,0.00005435939,0.000004717659,0.00001385389,0.00000340547,0.000233987],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9662377,"threshold_uncertainty_score":0.2626867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04530492390956928,"score_gpt":0.3018490734310908,"score_spread":0.2565441495215215,"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."}}