{"id":"W2187865221","doi":"","title":"SIGNED LIKELIHOOD ROOT WITH A SIMPLE SKEWNESS CORRECTION: REGULAR MODELS, SECOND ORDER","year":2013,"lang":"en","type":"article","venue":"Journal of Statistical Research","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Root (linguistics); Maximum likelihood; Mathematics; Simple (philosophy); Inference; Likelihood function; Skewness; Statistics; Likelihood-ratio test; Econometrics; Statistical inference; Algorithm; Computer science; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002517261,0.0002153692,0.0006022862,0.0002344252,0.0002002524,0.0001608946,0.0003295226,0.0001265433,0.001918503],"category_scores_gemma":[0.006761429,0.0001445611,0.00006217355,0.0005197822,0.0003616148,0.0004705446,0.000105359,0.001173579,0.00003880804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001521052,"about_ca_system_score_gemma":0.0004212137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004524018,"about_ca_topic_score_gemma":0.00006843303,"domain_scores_codex":[0.9957906,0.0006756165,0.000793035,0.0003021613,0.001640021,0.0007985604],"domain_scores_gemma":[0.9866957,0.008608741,0.0002313836,0.0003288547,0.003475405,0.0006599047],"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":[0.0008306894,0.0007590359,0.00004055714,0.0003182675,0.0001995553,0.0004476771,0.0004327183,0.0003695303,0.001083943,0.7861031,0.08167434,0.1277406],"study_design_scores_gemma":[0.0009281069,0.001368631,0.0001462635,0.0001065368,0.000035696,0.0001743394,0.0003897097,0.03345692,0.0002508045,0.9617304,0.001225812,0.0001867479],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005732787,0.00007479576,0.9898272,0.0003723486,0.0001342233,0.0004141688,0.00004928384,0.00001821815,0.003376969],"genre_scores_gemma":[0.2550513,0.00001405284,0.742738,0.00004135464,0.000194951,0.00003881012,0.000003538407,0.00005571587,0.001862279],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2493185,"threshold_uncertainty_score":0.9989939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.156819195091797,"score_gpt":0.4675893320779697,"score_spread":0.3107701369861727,"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."}}