{"id":"W4404130423","doi":"10.1145/3695835","title":"Computing A Well-Representative Summary of Conjunctive Query Results","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ACM on Management of Data","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Conjunctive query; Boolean conjunctive query; Computer science; Query optimization; Query expansion; Information retrieval; Theoretical computer science; Web search query; Sargable; Data mining; Search engine; Relational database","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0008256802,0.0001406818,0.0002499656,0.0001227225,0.00005312201,0.00003632825,0.00440826,0.00002293215,0.000001912833],"category_scores_gemma":[0.0002457383,0.00009851893,0.00006151331,0.0005959872,0.0001507444,0.001239496,0.01165233,0.000115092,0.000004753233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001891235,"about_ca_system_score_gemma":0.00001857197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005359937,"about_ca_topic_score_gemma":0.000001044064,"domain_scores_codex":[0.9982798,0.00001475004,0.0004924398,0.0005642483,0.0004875518,0.0001612146],"domain_scores_gemma":[0.9974471,0.0002120127,0.0004178621,0.001756399,0.0001403466,0.0000262334],"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":[0.0002018753,0.0001541204,0.0003507384,0.003249928,0.0005633752,0.0000082499,0.001544159,0.00007353749,0.002572304,0.8498114,0.1224486,0.01902164],"study_design_scores_gemma":[0.006696623,0.002053833,0.01630637,0.04686313,0.0009279915,0.0000408388,0.03274987,0.1321786,0.3007231,0.09916464,0.3598875,0.002407426],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1600652,0.005846035,0.4375251,0.02835225,0.006383163,0.008554295,0.008987875,0.00127542,0.3430107],"genre_scores_gemma":[0.6875433,0.000292335,0.3102241,0.0001125841,0.00009967228,0.00001191507,0.00008714744,0.00002741514,0.001601552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7506468,"threshold_uncertainty_score":0.9963412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05242105394825337,"score_gpt":0.3208429987592012,"score_spread":0.2684219448109479,"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."}}