{"id":"W2937254335","doi":"10.11575/prism/31331","title":"Modelling and Design of Generic Semantic Trajectory Data Warehouse","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Data warehouse; Trajectory; Data mining; Ontology; Geospatial analysis; Context (archaeology); Semantic data model; Inference; Object (grammar); Information retrieval; Data science; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004080638,0.0002253008,0.0003004663,0.0002125858,0.000151645,0.0002058892,0.005137051,0.0001134872,0.000003902422],"category_scores_gemma":[0.00001431279,0.0002549102,0.000051686,0.000142707,0.0001230993,0.001088594,0.007989917,0.0002325055,0.00000989773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002705328,"about_ca_system_score_gemma":0.00009761327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002208995,"about_ca_topic_score_gemma":0.000007185596,"domain_scores_codex":[0.9983116,0.00008954879,0.0001546709,0.001125054,0.00009032388,0.0002288276],"domain_scores_gemma":[0.9958482,0.00005933966,0.000299729,0.003652518,0.00005357929,0.00008660556],"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":[0.000007400858,0.00004270799,0.0001479293,0.0001349163,0.00009056033,0.0001649598,0.00008329132,0.9911553,0.000006320057,0.005754954,0.0004110207,0.002000671],"study_design_scores_gemma":[0.0002209022,0.00002079696,0.00006691238,0.00007537111,0.0000902527,0.000001375594,0.0000134928,0.9917131,0.00002256763,0.007304823,0.0002033786,0.0002670194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01336487,0.0002165472,0.9854471,0.00002381296,0.0003030817,0.0002351457,0.00004701935,0.0001052388,0.0002571557],"genre_scores_gemma":[0.9482034,0.001646201,0.0492681,0.00001544723,0.00005063562,3.149799e-7,0.0000546108,0.00001596448,0.0007452736],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.936179,"threshold_uncertainty_score":0.9999903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.270021894141485,"score_gpt":0.2135042860660422,"score_spread":0.05651760807544273,"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."}}