{"id":"W2138794734","doi":"10.1109/icde.2006.88","title":"Making Designer Schemas with Colors","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Schema (genetic algorithms); XML; Intuition; Data redundancy; Redundancy (engineering); Information retrieval; Theoretical computer science; Data mining; XML Schema (W3C); Document Structure Description; Programming language; Database; Document type definition; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008870814,0.0007231171,0.0005342899,0.0009316977,0.0006380845,0.004601095,0.002253613,0.00159214,0.003648082],"category_scores_gemma":[0.02208765,0.001203889,0.001449116,0.001108554,0.002890591,0.009828626,0.003541066,0.002620583,0.001581891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001210522,"about_ca_system_score_gemma":0.002148194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002084563,"about_ca_topic_score_gemma":0.002357943,"domain_scores_codex":[0.99213,0.00335004,0.0008408533,0.001245732,0.002051979,0.0003813722],"domain_scores_gemma":[0.9830201,0.004622495,0.0009934059,0.008107901,0.00295969,0.0002963335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001890615,0.00008636803,0.002520311,0.0003732684,0.00007213855,0.000345583,0.003188362,0.01866494,0.01950424,0.8428177,0.006748507,0.1054896],"study_design_scores_gemma":[0.0001646239,0.0001918431,0.0004390277,0.0002184043,0.0001905349,0.0009606014,0.001307908,0.07890585,0.08726224,0.3246095,0.5056425,0.0001069641],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00852487,0.00008517215,0.9839213,0.0006039786,0.00006713477,0.0001246897,0.0001442341,0.002400524,0.004128018],"genre_scores_gemma":[0.1050183,0.0003107057,0.8856401,0.000593836,0.000038855,0.000249935,0.0004031578,0.0009852586,0.006759837],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008870814,"threshold_uncertainty_score":0.04691386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104230506838537,"score_gpt":0.2459990558829333,"score_spread":0.224956750814548,"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."}}