{"id":"W2797284464","doi":"10.5539/ijsp.v7n4p50","title":"On Optimal Allocation of Treatment/Condition Variance in Principal Component Analysis","year":2018,"lang":"en","type":"preprint","venue":"International Journal of Statistics and Probability","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Principal component analysis; Variance (accounting); Econometrics; Optimal allocation; Principal (computer security); Mathematics; Statistics; Mathematical optimization; Computer science; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03331627,0.00202118,0.002524166,0.001326897,0.001227426,0.002198378,0.001540667,0.002470673,0.003057558],"category_scores_gemma":[0.1180648,0.001688793,0.001288489,0.002009128,0.006381548,0.003786256,0.004460134,0.003879157,0.00137812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001839161,"about_ca_system_score_gemma":0.003350766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001408251,"about_ca_topic_score_gemma":0.001363882,"domain_scores_codex":[0.9639553,0.02722592,0.001612489,0.003701273,0.002646858,0.0008581099],"domain_scores_gemma":[0.9541281,0.03472876,0.001954898,0.005873846,0.002827028,0.0004872895],"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":[0.002003272,0.0004169129,0.002328452,0.000893918,0.0004411962,0.0001565899,0.001086826,0.1342857,0.02314936,0.3866384,0.003712762,0.4448868],"study_design_scores_gemma":[0.0004139075,0.0003803064,0.003745275,0.0002709219,0.0001764839,0.0001335247,0.0001629701,0.4120791,0.01376603,0.5646265,0.004090396,0.0001547199],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008893798,0.0003582671,0.9889787,0.0003428552,0.00004354916,0.0001402596,0.00003994181,0.0001237385,0.001078913],"genre_scores_gemma":[0.1342149,0.0007524434,0.8618432,0.0003846137,0.0001415253,0.0009623274,0.000186695,0.0003181006,0.001196116],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03331627,"threshold_uncertainty_score":0.1761954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02598711900718724,"score_gpt":0.3273935404164359,"score_spread":0.3014064214092486,"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."}}