{"id":"W4242639621","doi":"10.22215/etd/2017-12163","title":"Classification and Feature Selection in Sparse High-Dimensional Models","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Dimension (graph theory); Feature selection; Computer science; Selection (genetic algorithm); Binary number; Data mining; Feature (linguistics); Artificial intelligence; Population; Machine learning; High dimensional; Pattern recognition (psychology); Algorithm; Mathematics; Combinatorics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002548434,0.000179965,0.0002944676,0.0001056108,0.00009739034,0.00006264109,0.0000852724,0.0003780712,0.0001629786],"category_scores_gemma":[0.0006927753,0.0001508328,0.00002575312,0.00005167508,0.00002304425,0.00009728225,0.00000873882,0.0003492269,0.000006488071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004101999,"about_ca_system_score_gemma":0.0000726644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001609966,"about_ca_topic_score_gemma":0.0009343601,"domain_scores_codex":[0.999064,0.00006952166,0.0002178777,0.0003134398,0.0001954274,0.0001397435],"domain_scores_gemma":[0.9991347,0.000320571,0.0002069584,0.0001587147,0.0001273953,0.00005168471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006893588,0.00006134701,0.0001075238,0.0001872242,0.00001475703,0.000002135405,0.0001274181,0.000004611672,0.001153548,0.9632904,0.00349504,0.03148704],"study_design_scores_gemma":[0.0002279477,0.00003503144,0.04489431,0.0001963899,0.00004052547,0.000003085214,0.00006908477,0.03883613,0.0003063695,0.9151123,0.00006959433,0.0002092567],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.715596,0.0003356613,0.07217623,0.0007350464,0.001498202,0.001747634,0.0001214359,0.0002515963,0.2075382],"genre_scores_gemma":[0.4573818,0.00007751616,0.4883022,0.00004269374,0.0001192437,0.00008008776,0.0002992796,0.00004823809,0.05364894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.416126,"threshold_uncertainty_score":0.6150783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.12989105040511,"score_gpt":0.3974270702613396,"score_spread":0.2675360198562297,"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."}}