{"id":"W1557643481","doi":"10.1002/9780470987605.ch10","title":"Improving Data Behaviour for Statistical Analysis: Ranking and Transformations","year":2008,"lang":"en","type":"other","venue":"","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Ranking (information retrieval); Sorting; Multivariate statistics; Transformation (genetics); Data set; Set (abstract data type); Computer science; Data transformation; Data mining; Mathematics; Statistics; Information retrieval; Algorithm; Artificial intelligence; Chemistry; Data warehouse","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":[0.01148715,0.001502696,0.001904364,0.002932812,0.0009272829,0.004774585,0.001563025,0.0007860145,0.01269375],"category_scores_gemma":[0.07543404,0.0007721463,0.001813246,0.005181804,0.001791588,0.005493204,0.002443839,0.004055869,0.009078974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001480586,"about_ca_system_score_gemma":0.002748193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00101454,"about_ca_topic_score_gemma":0.001080079,"domain_scores_codex":[0.9799202,0.009877893,0.001656212,0.001994279,0.006166808,0.0003845795],"domain_scores_gemma":[0.9520253,0.02661201,0.001524439,0.01348264,0.006003225,0.0003524686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001869184,0.0001263723,0.001152985,0.0005815447,0.00005842176,0.00006364518,0.0002836155,0.01486261,0.01102651,0.1001094,0.0200485,0.8514995],"study_design_scores_gemma":[0.00008832413,0.0002945934,0.00262189,0.0002731496,0.00008907631,0.0003824476,0.0003284435,0.3586943,0.05768589,0.5113072,0.06810329,0.0001312917],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001582293,0.0002045854,0.9931989,0.000269595,0.00006297472,0.00009143617,0.000203083,0.003085858,0.001301214],"genre_scores_gemma":[0.03509396,0.0006089974,0.9564713,0.0002052032,0.0001661677,0.0003660086,0.001047416,0.002311184,0.003729754],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01269375,"threshold_uncertainty_score":0.0607506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04472685354965211,"score_gpt":0.2993347018271038,"score_spread":0.2546078482774517,"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."}}