{"id":"W2489493656","doi":"10.1016/b978-008045374-3/50011-9","title":"A Data Warehouse Strategy for on-Demand Multiscale Mapping","year":2007,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre de Géomatique du Québec; Université Laval","funders":"","keywords":"Computer science; Cartographic generalization; Data warehouse; Generalization; Context (archaeology); Representation (politics); Abstraction; Architecture; Data science; Data mining; Database; Geography","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":[0.0007881714,0.0004909114,0.0004760527,0.0003104764,0.0001920314,0.0004283194,0.003873651,0.0002239179,0.00003146932],"category_scores_gemma":[0.0000180365,0.0004578948,0.0001514678,0.00002633542,0.00008091496,0.0004131826,0.001845492,0.0003597638,0.0002524887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004735422,"about_ca_system_score_gemma":0.00006827728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.11371e-7,"about_ca_topic_score_gemma":0.00002472608,"domain_scores_codex":[0.9972105,0.0000153597,0.0005094062,0.0012803,0.0004705873,0.0005138625],"domain_scores_gemma":[0.9961766,0.0001527328,0.0002766698,0.003170926,0.00006981097,0.0001532572],"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":[0.000005272459,0.00001276117,2.863484e-7,0.00008959846,0.00008929174,0.00005928625,0.0000524632,0.000003727269,9.14376e-7,0.04017043,0.004391344,0.9551246],"study_design_scores_gemma":[0.0004660003,0.00009818115,0.000004957027,0.0003071038,0.00004007505,0.000005222385,0.000004941481,0.006404559,0.000006377945,0.01136639,0.980745,0.0005512056],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[4.946868e-7,0.0003638202,0.1137419,0.00008359146,0.0006340771,0.001044705,0.0006202302,0.0002390518,0.8832722],"genre_scores_gemma":[0.00002165425,0.00006608412,0.04444421,0.000627532,0.000613127,0.00003133131,0.0005106459,0.00007658494,0.9536088],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9763536,"threshold_uncertainty_score":0.9997873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1282891837885225,"score_gpt":0.3107184563530914,"score_spread":0.1824292725645689,"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."}}