{"id":"W2042315498","doi":"10.1145/581751.581752","title":"Searching for dependencies at multiple abstraction levels","year":2002,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Functional dependency; Dependency (UML); Tuple; Online analytical processing; Dependency theory (database theory); Generalization; Abstraction; Scope (computer science); Data mining; Extension (predicate logic); Theoretical computer science; Database; Data warehouse; Relational database; Artificial intelligence; Programming language","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"],"consensus_categories":[],"category_scores_codex":[0.0004537154,0.0002640716,0.0002796368,0.0002205943,0.0008953732,0.0001276028,0.0006824078,0.00008174354,0.00005852391],"category_scores_gemma":[0.0001607037,0.0002453422,0.0001350841,0.0002888596,0.0000498614,0.001759655,0.0000510284,0.0002037979,0.0002591049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002099556,"about_ca_system_score_gemma":0.00002598001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004298564,"about_ca_topic_score_gemma":0.0006068511,"domain_scores_codex":[0.9977934,0.0001099709,0.0004641685,0.0006811984,0.0004704962,0.0004807751],"domain_scores_gemma":[0.9968196,0.0008098679,0.000162133,0.001920209,0.0001085617,0.0001796491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004734446,0.002656349,0.0005323783,0.004088048,0.001076546,0.0003375979,0.007764148,0.150105,0.1976909,0.1709131,0.02488816,0.4394743],"study_design_scores_gemma":[0.00236962,0.0003227882,0.0002215298,0.0005045617,0.00004686559,0.0005155481,0.0008952114,0.3263606,0.0311365,0.0001533935,0.6363226,0.001150741],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002785537,0.0003029949,0.9915824,0.0002811724,0.001543015,0.0008352023,0.001993155,0.000362182,0.0003143602],"genre_scores_gemma":[0.8708889,0.00007693413,0.1249821,0.00007769648,0.0001831742,0.0005166975,0.0001176885,0.00004031917,0.003116393],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8681034,"threshold_uncertainty_score":0.9999999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09694704259971397,"score_gpt":0.2879969122087368,"score_spread":0.1910498696090228,"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."}}