{"id":"W2377317762","doi":"","title":"Design of Data Warehouse in Network Instruction Quality Analysis System","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data warehouse; GRASP; Disadvantage; Table (database); Quality (philosophy); Realization (probability); Field (mathematics); SQL; Class (philosophy); Database; Sql server; Data mining; Software engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002523887,0.0006122899,0.0009710208,0.001999978,0.0007764742,0.003816653,0.002317572,0.0006170757,0.003768932],"category_scores_gemma":[0.003689823,0.0007431988,0.000696171,0.002188419,0.0003585354,0.002854527,0.001388875,0.0008446512,0.002257424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001082313,"about_ca_system_score_gemma":0.001592163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003354357,"about_ca_topic_score_gemma":0.001456348,"domain_scores_codex":[0.9978914,0.0002518205,0.0003499734,0.000614666,0.0007333389,0.000158796],"domain_scores_gemma":[0.9975166,0.0003168221,0.0001866028,0.0004701298,0.001333871,0.0001760077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001870937,0.001210875,0.04950853,0.00165126,0.0007092222,0.00151238,0.002649299,0.03592641,0.07923967,0.03410252,0.1272219,0.6643971],"study_design_scores_gemma":[0.0005773086,0.0005825728,0.01810446,0.0002342541,0.0007580161,0.001728559,0.0009644222,0.6115687,0.1616082,0.01378285,0.1897479,0.0003426909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0383414,0.0005072504,0.9108328,0.0008477859,0.0001949249,0.001379253,0.002779871,0.03767549,0.007441248],"genre_scores_gemma":[0.4319733,0.0007787952,0.5331168,0.0009145099,0.00021807,0.001659082,0.01712311,0.001195937,0.01302031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003816653,"threshold_uncertainty_score":0.01334774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05728791198695247,"score_gpt":0.3416662152549113,"score_spread":0.2843783032679588,"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."}}