{"id":"W2097000000","doi":"10.1109/icde.2005.110","title":"Predicate Derivation and Monotonicity Detection in DB2 UDB","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Computer science; Predicate (mathematical logic); Rewriting; Schema (genetic algorithms); Search engine indexing; Database; Data mining; Programming language; Information retrieval","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":[0.0001115275,0.0000445785,0.00005263224,0.00004670522,0.00003955103,0.00001818515,0.0000610511,0.00002110321,0.000003620798],"category_scores_gemma":[0.00001694637,0.0000384812,0.000006454056,0.000121208,0.00001359521,0.0008716314,0.00007046369,0.00004064917,0.000008230183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002348238,"about_ca_system_score_gemma":0.00000841961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001208356,"about_ca_topic_score_gemma":0.0007312338,"domain_scores_codex":[0.999563,0.00001560087,0.0001101645,0.00015846,0.00006462535,0.0000881766],"domain_scores_gemma":[0.9997623,0.00001513517,0.00002879976,0.0001539629,0.00001334007,0.00002648618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001013326,0.00004336768,0.003290096,0.00002101671,0.000004152126,0.000002783059,0.0006990822,0.0007193967,0.01862395,0.1972663,0.00006891486,0.7792508],"study_design_scores_gemma":[0.0008525451,0.00008819835,0.09658645,0.00003469839,0.000001818129,0.00003500673,0.00007941313,0.6308582,0.1775337,0.002528393,0.09108281,0.0003187476],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2084668,0.00004130018,0.7904712,0.0003234634,0.00003388225,0.00006934916,5.583387e-7,0.00006445454,0.000528958],"genre_scores_gemma":[0.9159271,0.00002119258,0.08382596,0.00009984615,0.00002707835,0.00001153166,7.389228e-7,0.00000173135,0.00008481642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.778932,"threshold_uncertainty_score":0.1569218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00816891070638493,"score_gpt":0.2266155199875499,"score_spread":0.218446609281165,"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."}}