{"id":"W2099525917","doi":"10.1007/s00778-009-0157-y","title":"A framework for testing DBMS features","year":2009,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Database; Generator (circuit theory); Set (abstract data type); Feature (linguistics); Test case; Test suite; Test (biology); Data mining; Programming language; Power (physics); Machine learning","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":[0.0004561003,0.00008008518,0.00009817517,0.00003157966,0.0005082678,0.0001543938,0.0004782283,0.00002866079,0.000002635441],"category_scores_gemma":[0.0004394499,0.00004461407,0.00005465537,0.0001840461,0.00002164062,0.000367068,0.00004760231,0.0002706641,0.000005696309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001513079,"about_ca_system_score_gemma":0.00004210469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002140059,"about_ca_topic_score_gemma":7.306513e-7,"domain_scores_codex":[0.9993231,0.00003576771,0.0001506342,0.0001085444,0.0001533267,0.0002285954],"domain_scores_gemma":[0.9991007,0.0003193561,0.000130795,0.0002994184,0.0000863788,0.00006338401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000009900504,0.00001437142,0.00003406098,0.000003461799,0.000008713695,0.00001426788,0.0005820711,0.0002453426,0.0008840947,0.8091173,0.004034548,0.1850519],"study_design_scores_gemma":[0.0004272451,0.0004232914,0.005038683,0.0003048783,0.00001214498,0.002719642,0.0002005978,0.004521076,0.001537386,0.8711542,0.1133644,0.0002963657],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00125997,0.0006733028,0.9924917,0.004579098,0.0004308587,0.00008990974,0.000002754778,0.0000536399,0.0004187361],"genre_scores_gemma":[0.07231183,0.00001414684,0.9252758,0.001256444,0.000938075,0.000003716235,3.371738e-7,0.000005050097,0.0001945927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1847555,"threshold_uncertainty_score":0.3909237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0426218733597992,"score_gpt":0.3124691026543456,"score_spread":0.2698472292945464,"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."}}