{"id":"W2082534709","doi":"10.1016/j.is.2012.08.005","title":"Consistent query answering under spatial semantic constraints","year":2012,"lang":"en","type":"article","venue":"Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Spatial query; Class (philosophy); Set (abstract data type); Data integrity; Semantics (computer science); Information retrieval; Consistency (knowledge bases); Core (optical fiber); Query optimization; Query language; Theoretical computer science; Database; Web query classification; Web search query; Artificial intelligence; Programming language; Search engine","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004602696,0.00009118882,0.00010822,0.0001242087,0.00008289126,0.0004917167,0.0003137668,0.00003321637,0.00001187144],"category_scores_gemma":[0.00001260052,0.00008200604,0.00003142762,0.0001377578,0.00003271531,0.00559173,0.000150677,0.00005184911,0.0008481905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003853339,"about_ca_system_score_gemma":0.00002089154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001085078,"about_ca_topic_score_gemma":0.000001561726,"domain_scores_codex":[0.9990241,0.00003068236,0.0003452138,0.00007106919,0.0002848421,0.0002441277],"domain_scores_gemma":[0.9993735,0.00002674241,0.0001522468,0.0003079473,0.0000564992,0.00008307883],"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":[0.00000373881,0.00006420144,0.006035706,0.0003836855,0.0001169515,0.000003919314,0.004139316,0.001081149,0.000070104,0.7935723,0.007661781,0.1868672],"study_design_scores_gemma":[0.001941936,0.00008161049,0.03282612,0.0003521066,0.00003980731,0.0002797193,0.005782181,0.5754382,0.0005842492,0.0002402655,0.3812126,0.001221175],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002772579,0.00004081293,0.9740746,0.0001167555,0.002506078,0.0002249734,0.000006461018,0.0001757451,0.02008199],"genre_scores_gemma":[0.9981017,0.000003390023,0.001348954,0.0002596895,0.0001268584,0.0000146277,0.00002771848,0.000002524276,0.000114528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9953291,"threshold_uncertainty_score":0.9999298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02100301934720149,"score_gpt":0.2307467823017295,"score_spread":0.209743762954528,"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."}}