{"id":"W1986041862","doi":"10.14778/1920841.1920982","title":"An access cost-aware approach for object retrieval over multiple sources","year":2010,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Overhead (engineering); Probabilistic logic; Object (grammar); Source code; Selection (genetic algorithm); Data mining; Information retrieval; Data source; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005437776,0.0001724305,0.0001761052,0.00009254317,0.0002079675,0.000563176,0.003509063,0.00005483678,0.000007937582],"category_scores_gemma":[0.00009326958,0.000117965,0.0001099542,0.0003525133,0.00007339122,0.001692704,0.001061764,0.0001680879,0.000001592048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002233826,"about_ca_system_score_gemma":0.00002043598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003986216,"about_ca_topic_score_gemma":0.000003684685,"domain_scores_codex":[0.9985462,0.00000473504,0.0002306102,0.000466068,0.0004288551,0.0003234934],"domain_scores_gemma":[0.999108,0.00004101667,0.0002352547,0.0003747485,0.0001580509,0.00008299716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000665523,0.004382917,0.1880685,0.001681419,0.0007031418,0.000002190067,0.007260485,0.0005186587,0.3769864,0.2073159,0.05414492,0.15827],"study_design_scores_gemma":[0.003126286,0.0003518215,0.02122373,0.00005165187,0.00008861919,0.000008461264,0.0004661838,0.4565867,0.4877375,0.005141393,0.02447739,0.0007402235],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8018389,0.00005656899,0.1800576,0.001575143,0.002306732,0.006093999,0.0001381389,0.0005717958,0.007361093],"genre_scores_gemma":[0.9644738,0.00000581173,0.03468127,0.0001859425,0.0001960241,0.0001170833,0.00001109933,0.00001563344,0.0003133454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4560681,"threshold_uncertainty_score":0.6520771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02476775960382951,"score_gpt":0.279069790039691,"score_spread":0.2543020304358615,"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."}}