{"id":"W1592716016","doi":"10.1007/bfb0027525","title":"Database management systems for statistical and scientific applications: Are commercially available DBMS good enough?","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Computer science; Database; Relation (database); Relational database; Data science; Set (abstract data type); Information system; Relational database management system; Engineering","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001326406,0.0005167986,0.0006082339,0.0005965855,0.0008199422,0.001064121,0.001778957,0.0001629471,0.00001313914],"category_scores_gemma":[0.0000436876,0.0004784398,0.00005971318,0.000460598,0.001037913,0.001025337,0.001660644,0.0003711202,0.00007241916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000230923,"about_ca_system_score_gemma":0.0002875969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002692277,"about_ca_topic_score_gemma":0.0001199756,"domain_scores_codex":[0.9955691,0.00003570593,0.000673494,0.002163241,0.0008372842,0.0007212475],"domain_scores_gemma":[0.9964506,0.0005536749,0.0004068821,0.00203763,0.0003097419,0.0002414853],"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":[0.000006494802,0.00004046033,0.00001105052,0.0004410859,0.00001599258,0.00003192755,0.000100533,0.001995443,0.00001967397,0.7611398,0.0005872948,0.2356103],"study_design_scores_gemma":[0.0005534876,0.0001089815,0.00002514151,0.0009805002,0.00002802602,0.00008696561,0.000002176368,0.2039899,0.00006789584,0.01856062,0.7746572,0.000939088],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001469387,0.004691374,0.9894392,0.000295519,0.001329382,0.002072065,0.0006014428,0.0001410789,0.00142846],"genre_scores_gemma":[0.001212888,0.0002927421,0.9934488,0.0003517481,0.0005986406,0.0003340621,0.0001826034,0.00004410556,0.003534387],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7740699,"threshold_uncertainty_score":0.9999729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02379675388021943,"score_gpt":0.2627817110291686,"score_spread":0.2389849571489492,"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."}}