{"id":"W3122698305","doi":"","title":"The Smooth Colonel and the Reverend Find Common Ground","year":2013,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Estimator; Generalization; Nonparametric regression; Mathematics; Nonparametric statistics; Regression; Kernel regression; Kernel (algebra); Econometrics; Panel data; Class (philosophy); Regression analysis; Statistics; Applied mathematics; Computer science; Artificial intelligence; Combinatorics; Mathematical analysis","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":["metaresearch","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.0338215,0.0003377868,0.0008790863,0.0005180871,0.001295216,0.002860524,0.003907535,0.0004012068,0.0001488471],"category_scores_gemma":[0.009407019,0.000183755,0.0003037165,0.0005251977,0.003746744,0.0001678137,0.004035943,0.002546764,0.0001112966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004401987,"about_ca_system_score_gemma":0.0005076023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009473475,"about_ca_topic_score_gemma":0.003965776,"domain_scores_codex":[0.9918009,0.00305751,0.001497352,0.00135953,0.001397707,0.0008869864],"domain_scores_gemma":[0.9707596,0.02475855,0.0006435973,0.003287868,0.0003718927,0.0001784991],"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":[0.0004734232,0.000174348,0.01112619,0.00003846899,0.000265566,0.00002600246,0.003164844,0.0171348,0.000024044,0.01791936,0.00537263,0.9442803],"study_design_scores_gemma":[0.002260193,0.00012806,0.1055533,0.0002088281,0.00006118764,0.00002731127,0.005675714,0.2459724,0.00002033539,0.3903162,0.2488507,0.0009257763],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9178317,0.00129126,0.00000675271,0.007999891,0.0005764765,0.001112892,0.00002897479,0.00001904662,0.07113303],"genre_scores_gemma":[0.9747302,0.009817281,0.0001391046,0.0002487226,0.0001663422,0.0002029163,0.000007852354,0.00003555729,0.01465201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9433545,"threshold_uncertainty_score":0.9997544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0902821351801732,"score_gpt":0.3942783079882607,"score_spread":0.3039961728080876,"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."}}