{"id":"W2946309990","doi":"10.1007/978-3-030-18305-9_62","title":"Principal Sample Analysis for Data Ranking","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Numerosity adaptation effect; Computer science; Reduction (mathematics); Ranking (information retrieval); Data reduction; Cluster analysis; Sample (material); Artificial intelligence; Data mining; Principal component analysis; Sample space; Selection (genetic algorithm); Principal (computer security); Field (mathematics); Pattern recognition (psychology); Machine learning; Mathematics","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":[0.006786094,0.002310721,0.003401077,0.00350912,0.001237372,0.002868625,0.00224915,0.001557754,0.01294099],"category_scores_gemma":[0.02470755,0.0009945608,0.002518889,0.005533582,0.001242804,0.002480811,0.002285888,0.003367696,0.01404124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007959357,"about_ca_system_score_gemma":0.001766508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001274835,"about_ca_topic_score_gemma":0.001796783,"domain_scores_codex":[0.9895608,0.005268356,0.0005950838,0.001193439,0.003099002,0.0002831589],"domain_scores_gemma":[0.9869241,0.007878107,0.000387489,0.003039214,0.001613171,0.0001577834],"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":[0.0001976016,0.0001088404,0.0006096212,0.0005579121,0.0002218243,0.00007314266,0.00008526665,0.01505721,0.002990793,0.03789766,0.04288338,0.8993167],"study_design_scores_gemma":[0.00008018676,0.0002283117,0.001952897,0.0001267618,0.0001648138,0.0003214627,0.0001155153,0.6975383,0.007580964,0.2376586,0.05413614,0.00009598755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005991576,0.0009688537,0.9954566,0.0001453647,0.000124813,0.00005733585,0.0003314864,0.001727211,0.0005891911],"genre_scores_gemma":[0.02637084,0.001315315,0.9618707,0.0001599023,0.0004325791,0.00059994,0.002661749,0.0009503865,0.005638711],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01294099,"threshold_uncertainty_score":0.04329199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05391410543757578,"score_gpt":0.3056288085389437,"score_spread":0.2517147031013679,"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."}}