{"id":"W7014791640","doi":"","title":"Proceedings of the Thirtieth International Conference on Very Large Data Bases Toronto, Canada, August 31-September 3, 2004","year":2004,"lang":"en","type":"article","venue":"","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Information system; Field (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004929981,0.001836445,0.002445609,0.001886832,0.004080533,0.008529548,0.002361497,0.001122636,0.1095998],"category_scores_gemma":[0.005849173,0.0009954765,0.0009951787,0.003057962,0.002449218,0.003240699,0.002969664,0.003560348,0.04333473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007409906,"about_ca_system_score_gemma":0.02062951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3941722,"about_ca_topic_score_gemma":0.5946841,"domain_scores_codex":[0.9978119,0.0003412156,0.00009543149,0.0002333784,0.001192473,0.0003255983],"domain_scores_gemma":[0.9903875,0.001024269,0.0001355341,0.001404196,0.004805983,0.002242503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003675657,0.0001435049,0.001500992,0.0001385993,0.00006402386,0.0001239506,0.0002462688,0.0006634316,0.002301064,0.003689396,0.8965797,0.0941815],"study_design_scores_gemma":[0.00009357536,0.00007193218,0.004083823,0.0001139861,0.00009803968,0.000143626,0.0004519228,0.009073914,0.002715033,0.00506712,0.9780287,0.00005845457],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.04247865,0.04719545,0.3630424,0.064666,0.09033999,0.002783993,0.02866096,0.03595116,0.3248814],"genre_scores_gemma":[0.0440386,0.0169526,0.05812588,0.002266961,0.003920167,0.0003534193,0.03188194,0.003140225,0.8393202],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.3941722,"threshold_uncertainty_score":0.7837557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1479580104865979,"score_gpt":0.3636700339404246,"score_spread":0.2157120234538267,"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."}}