{"id":"W2125619608","doi":"10.1109/vis.2003.10005","title":"KMVQL: a Graphical User Interface for Boolean Query Specification and Query Result Visualization","year":2003,"lang":"en","type":"article","venue":"IEEE Visualization","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Construct (python library); Window (computing); Query language; Visualization; Information retrieval; Query by Example; And-inverter graph; Query optimization; Graphical user interface; Boolean conjunctive query; Interface (matter); Data mining; Data visualization; Theoretical computer science; Web search query; Boolean expression; Boolean function; Programming language; Algorithm; Search engine; World Wide Web","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":[0.002967386,0.001266691,0.0009807125,0.001576893,0.0005685794,0.003403914,0.002593415,0.001223995,0.04089973],"category_scores_gemma":[0.009799391,0.0008782867,0.0007579145,0.001379732,0.0005504015,0.004516803,0.002871113,0.001689275,0.01415249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008165443,"about_ca_system_score_gemma":0.001069243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004749543,"about_ca_topic_score_gemma":0.003785654,"domain_scores_codex":[0.9979839,0.0006509154,0.0002690515,0.0002554714,0.0007108987,0.0001297563],"domain_scores_gemma":[0.9958763,0.002172873,0.0001776228,0.0007485215,0.000788858,0.0002357658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001868568,0.000277164,0.004008688,0.001798786,0.0001931382,0.0006289902,0.001590997,0.007257855,0.02849718,0.04629422,0.6872646,0.22032],"study_design_scores_gemma":[0.0007250897,0.0002329842,0.002733257,0.0003454208,0.0001120388,0.001063493,0.0004968932,0.2026476,0.04110662,0.06747261,0.6827109,0.000353212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002691566,0.0002056216,0.6581218,0.0004459438,0.0000840778,0.0002437076,0.01120808,0.3178819,0.009117264],"genre_scores_gemma":[0.1534075,0.0007710889,0.6961085,0.001879741,0.0001836823,0.001821683,0.06141296,0.05833425,0.02608064],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04089973,"threshold_uncertainty_score":0.1368232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02473109762522597,"score_gpt":0.3196929198591682,"score_spread":0.2949618222339422,"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."}}