{"id":"W4283207738","doi":"10.1002/cjs.11706","title":"Distributed estimation with empirical likelihood","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Computer science; Divide and conquer algorithms; Consistency (knowledge bases); Asymptotic distribution; Normality; Empirical likelihood; Sample (material); Empirical research; Estimation; Statistics; Algorithm; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004979924,0.0001032516,0.0002351547,0.0001310224,0.000261473,0.0000467428,0.0001908096,0.000024596,0.001133176],"category_scores_gemma":[0.002198739,0.00008671253,0.00002729292,0.0002350762,0.00009261545,0.00004181346,0.0000150829,0.0003712457,0.000003401562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002531924,"about_ca_system_score_gemma":0.001515013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003184246,"about_ca_topic_score_gemma":0.001388485,"domain_scores_codex":[0.9987581,0.0001544989,0.0004001879,0.00008820227,0.0003266103,0.00027235],"domain_scores_gemma":[0.9978607,0.0009066164,0.0002807115,0.0001241446,0.0002899723,0.0005378478],"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":[0.0001270947,0.000147163,0.01220063,0.000113665,0.0001697003,0.002474768,0.001676351,0.001042096,0.00001498257,0.6306305,0.2553509,0.09605213],"study_design_scores_gemma":[0.000736251,0.001265352,0.008711306,0.00004494629,0.0001547148,0.0008013895,0.0006143741,0.01383543,0.00002186219,0.9603108,0.01324183,0.0002617718],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009510765,0.00003264395,0.9869443,0.0004422965,0.0002061149,0.00007423144,0.002457621,0.000005770798,0.0003262093],"genre_scores_gemma":[0.2849993,0.000001168994,0.7147294,0.0001559546,0.00003935286,0.000003831829,0.0000248261,0.00001591914,0.000030236],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3296803,"threshold_uncertainty_score":0.9997799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08054540798775184,"score_gpt":0.3362861210163824,"score_spread":0.2557407130286306,"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."}}