{"id":"W1563601331","doi":"10.1007/3-540-48196-6_8","title":"QFD Matrix for Incremental Construction of a Warehouse via Data Marts","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Data warehouse; Computer science; Quality function deployment; Construct (python library); Warehouse; Process (computing); Database; Software deployment; Data mining; Software engineering; Programming language; Operations management; 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"],"consensus_categories":[],"category_scores_codex":[0.0007692552,0.0004008541,0.0005588468,0.0005010097,0.0002016039,0.0001167094,0.002769934,0.0001858416,0.00001500373],"category_scores_gemma":[0.00008048992,0.0003679706,0.00008690937,0.0003753457,0.0007513469,0.001357108,0.00246738,0.0002976606,0.000007708147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001527511,"about_ca_system_score_gemma":0.0003524929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007361699,"about_ca_topic_score_gemma":0.0001502299,"domain_scores_codex":[0.9967112,0.0000237566,0.0006598757,0.001435998,0.0006993976,0.0004697738],"domain_scores_gemma":[0.9963513,0.0003146933,0.0004986853,0.002495113,0.0002285052,0.0001116852],"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":[0.00003269736,0.00004293626,0.0001068174,0.0002391694,0.0000311567,0.00005468372,0.0003427165,0.005075962,0.0006920823,0.1272627,0.0001504525,0.8659686],"study_design_scores_gemma":[0.001219224,0.0005653037,0.00004091393,0.001243547,0.00003621575,0.0008386123,0.000001668986,0.7649856,0.005681396,0.1343856,0.08943873,0.001563276],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003693795,0.00041078,0.9962005,0.000181056,0.001753849,0.0006491681,0.0002342541,0.0001049877,0.0004284783],"genre_scores_gemma":[0.007150301,0.00005747941,0.9919292,0.000173983,0.0004059518,0.00001170749,0.0001048113,0.0000298712,0.0001367002],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8644053,"threshold_uncertainty_score":0.9998772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02811866768293397,"score_gpt":0.2798995513708967,"score_spread":0.2517808836879628,"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."}}