{"id":"W2904270776","doi":"10.48550/arxiv.1812.05678","title":"Objective-Driven Ensembles: Bridging the Gap Between Interpretable Sparsity and Algorithmic Prediction","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Shrinkage; Estimator; Linear regression; Statistics; Mean squared error; Regression; Regression analysis; Mathematics; Lasso (programming language); Variance (accounting); Ridge; Proper linear model; Shrinkage estimator; Econometrics; Polynomial regression; Computer science; Minimum-variance unbiased estimator; Bias of an estimator; Geology; Economics","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":[0.000576957,0.0003113438,0.0004644048,0.0001160561,0.0003016677,0.00008845785,0.0004887292,0.0002813151,0.00007958702],"category_scores_gemma":[0.0005854116,0.0002742473,0.000121921,0.0001985025,0.0003987807,0.0001108518,0.00114375,0.0007184948,0.00002367159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001720393,"about_ca_system_score_gemma":0.00009093201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000215513,"about_ca_topic_score_gemma":0.00002772354,"domain_scores_codex":[0.9981468,0.0004037972,0.000249989,0.0007662612,0.0001035485,0.0003295512],"domain_scores_gemma":[0.9974902,0.001240675,0.0002737268,0.0006653072,0.0001913439,0.0001388039],"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":[0.000434338,0.0004854181,0.2318204,0.002385344,0.00283798,0.0003277043,0.007728098,0.003393591,0.0004398942,0.7161595,0.009354508,0.02463323],"study_design_scores_gemma":[0.000378862,0.0001302079,0.02024595,0.0004808522,0.0006338686,0.000007506014,0.000299041,0.2288926,0.0001681874,0.7481979,0.0001467003,0.0004183455],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3852599,0.00001672122,0.6116191,0.00002678838,0.0002416648,0.0003334472,0.0002235334,0.0001024914,0.002176374],"genre_scores_gemma":[0.9808644,0.00009762748,0.01818296,0.00002644434,0.000341206,0.000001798125,0.00001346384,0.0000276364,0.0004444317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5956046,"threshold_uncertainty_score":0.999971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1920038623723347,"score_gpt":0.2669424434138457,"score_spread":0.07493858104151105,"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."}}