{"id":"W4242283844","doi":"10.1007/978-1-4939-7131-2_101248","title":"Spatio-temporal Querying of Big Data and Results Summarization","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Automatic summarization; Computer science; Big data; Information retrieval; Data mining","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":[0.0004360978,0.0001514655,0.0001797912,0.0001641791,0.0000448821,0.0001719611,0.001346818,0.00008333229,0.0000350466],"category_scores_gemma":[0.00003075604,0.0001353801,0.00001729018,0.00004641268,0.00007060295,0.0007324728,0.002581507,0.0000666136,0.00003439223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000754101,"about_ca_system_score_gemma":0.00003398254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001029291,"about_ca_topic_score_gemma":0.0002337645,"domain_scores_codex":[0.9986833,0.000008789656,0.0003767837,0.00057148,0.0002562372,0.0001033709],"domain_scores_gemma":[0.9979773,0.00003390918,0.0003070377,0.001571559,0.00007274451,0.00003744978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001508204,0.00001447523,0.0001109203,0.00009860486,0.00007209896,0.00001152674,0.0001290783,5.773462e-7,0.000001732406,0.4534659,0.1264701,0.4196098],"study_design_scores_gemma":[0.0003003466,0.00006311332,0.00004318174,0.0001306569,0.00002298494,0.000001236923,0.000003891545,0.03645942,0.00001962105,0.01565829,0.9470329,0.0002643156],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000003633771,0.0000610373,0.4371276,0.0002633024,0.0005320664,0.0001586573,0.0003194597,0.00007711636,0.5614571],"genre_scores_gemma":[0.001367287,0.000338463,0.1134924,0.0001592083,0.0006421575,0.000001219318,0.007904488,0.00002748701,0.8760673],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8205628,"threshold_uncertainty_score":0.552064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08041800393860918,"score_gpt":0.258592237879088,"score_spread":0.1781742339404788,"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."}}