{"id":"W2160734757","doi":"10.1109/mmdbms.1996.541863","title":"Modeling of video spatial relationships in an object database management system","year":2002,"lang":"en","type":"article","venue":"","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Object-based spatial database; Computer science; Representation (politics); Spatial database; Set (abstract data type); Semantics (computer science); Inference; Object (grammar); Spatial query; Data modeling; Data model (GIS); Salient; Video tracking; Key (lock); Database; Data mining; Information retrieval; Spatial analysis; Artificial intelligence; Search engine; Web query classification; Programming language; 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":[0.0002218733,0.00005197132,0.00006743504,0.0001766571,0.00004671415,0.00003509046,0.0001596456,0.00002211847,0.00006384426],"category_scores_gemma":[0.000008993674,0.00005180135,0.00001566429,0.0002524189,0.000007185192,0.0005442214,0.00005596903,0.00006023726,0.00001874318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003956294,"about_ca_system_score_gemma":0.000005627495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001540322,"about_ca_topic_score_gemma":0.0005512818,"domain_scores_codex":[0.9993008,0.00007373209,0.0002241386,0.0001786233,0.0001399191,0.00008273311],"domain_scores_gemma":[0.9995782,0.00001950442,0.00003414869,0.0003023586,0.00003109281,0.00003468123],"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":[0.000003220958,0.00006722292,0.006418032,0.00005688661,0.000006846203,0.00001117933,0.0006706019,0.7395502,0.00003303985,0.2054091,0.00004015529,0.04773354],"study_design_scores_gemma":[0.0002036456,0.000009315382,0.00193785,0.00002992254,0.000002263848,0.000003141402,0.0002139479,0.9974638,0.00002108843,0.00005007694,0.000007485294,0.00005745848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00760424,0.000009114344,0.9807694,0.00009199601,0.00008983596,0.0001258848,0.000001201696,0.00009373154,0.01121459],"genre_scores_gemma":[0.8747117,0.000006325515,0.1251757,0.00001658418,0.000006976913,0.000004924067,0.000003695751,0.000002325574,0.00007180148],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8671075,"threshold_uncertainty_score":0.2112398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04990593347884132,"score_gpt":0.2418086297803717,"score_spread":0.1919026963015304,"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."}}