{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004020371,0.0005725133,0.0008909336,0.002163992,0.00119958,0.006990705,0.003081662,0.001519557,0.001402208],"category_scores_gemma":[0.005731679,0.0007786011,0.002526422,0.004009644,0.001416971,0.007552851,0.003719487,0.001622806,0.0008110942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002505212,"about_ca_system_score_gemma":0.004150962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009915097,"about_ca_topic_score_gemma":0.008709894,"domain_scores_codex":[0.9970381,0.0006007171,0.0006417005,0.0005882885,0.0008815026,0.0002496529],"domain_scores_gemma":[0.998053,0.0003645051,0.0002138006,0.0005486303,0.0006786949,0.0001414765],"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":[0.0001357941,0.0001582772,0.003332361,0.0004114294,0.0001340642,0.0009852849,0.001757242,0.2553087,0.007020135,0.670959,0.005673398,0.05412434],"study_design_scores_gemma":[0.00003314197,0.00004438635,0.0004302977,0.0001078757,0.00006802406,0.0003480072,0.0007127178,0.7734897,0.006751565,0.1522446,0.06571992,0.00004976314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005811438,0.00007369801,0.9900483,0.0003760482,0.00002951674,0.0001913669,0.0006900479,0.0006578901,0.002121698],"genre_scores_gemma":[0.1074169,0.0005108848,0.8848763,0.0001677477,0.00002997515,0.0004703922,0.003987789,0.0002070869,0.002333031],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009915097,"threshold_uncertainty_score":0.02126205,"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."}}