{"id":"W3195574125","doi":"","title":"Phase Transitions for High-Dimensional Quadratic Discriminant Analysis with Rare and Weak Signals","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Quadratic classifier; Mathematics; Linear discriminant analysis; Quadratic equation; Discriminant; Matrix (chemical analysis); Omega; Combinatorics; Scatter matrix; Covariance; Boundary (topology); Quadratic form (statistics); Artificial intelligence; Pattern recognition (psychology); Multivariate normal distribution; Applied mathematics; Multivariate statistics; Statistics; Physics; Support vector machine; Mathematical analysis; Computer science","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":[0.0002225918,0.0002934067,0.0007115116,0.0002085425,0.0001830818,0.00007272391,0.0001697286,0.0001679462,0.00027787],"category_scores_gemma":[0.0002132809,0.0002581552,0.0002162156,0.0004392203,0.0001725772,0.00007845822,0.0001388964,0.0002672065,0.000001236962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005150781,"about_ca_system_score_gemma":0.0001519401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009195248,"about_ca_topic_score_gemma":0.0002583046,"domain_scores_codex":[0.9984674,0.0001954384,0.0002516342,0.0007466191,0.00009351248,0.000245431],"domain_scores_gemma":[0.9977456,0.001153209,0.0001867084,0.0004634095,0.0002741075,0.0001769591],"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":[0.0003954102,0.00121485,0.0001800631,0.0009084843,0.002510589,0.0003557112,0.0005994153,0.01133632,0.0005535989,0.9808351,0.0001676909,0.0009427817],"study_design_scores_gemma":[0.002919235,0.0005794663,0.0007430202,0.000465408,0.0123062,0.000008280264,0.001406769,0.3152396,0.0004506382,0.6650696,0.00001268673,0.0007991819],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4043334,0.00002204935,0.5947435,0.000071173,0.00003471943,0.0002653496,0.0004103125,0.00002651551,0.00009303459],"genre_scores_gemma":[0.8752137,0.00002001239,0.1241915,0.00003045245,0.00002289674,0.000006615266,0.0001660017,0.00001994567,0.0003288484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4708804,"threshold_uncertainty_score":0.9999871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1648772904716151,"score_gpt":0.2865730770379131,"score_spread":0.121695786566298,"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."}}