{"id":"W1527212461","doi":"10.1002/bimj.201200200","title":"Modified Gaussian estimation for correlated binary data","year":2013,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Mathematics; Estimator; Statistics; Generalized estimating equation; Gaussian; Consistency (knowledge bases); Correlation; Binary data; Regression analysis; Binary number; Regression; Applied mathematics","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","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004994838,0.0001647848,0.0003272842,0.002139283,0.0003058369,0.00109861,0.001983047,0.000156142,0.001237537],"category_scores_gemma":[0.01496599,0.0001086363,0.0001316606,0.005251333,0.0001069474,0.001515566,0.0003703567,0.0002651143,0.0009610516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001087721,"about_ca_system_score_gemma":0.00009321004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001726995,"about_ca_topic_score_gemma":7.135267e-8,"domain_scores_codex":[0.9962466,0.0004040613,0.0009704176,0.0004948021,0.001497605,0.0003865279],"domain_scores_gemma":[0.9948614,0.002992402,0.0004284641,0.0008104231,0.0004491342,0.0004582206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001281741,0.0002977643,0.0004231861,0.00000282971,0.00004314263,0.00001682363,0.00006289576,0.0008404147,0.02605085,0.0006142336,0.1670772,0.8044425],"study_design_scores_gemma":[0.001083044,0.0005241086,0.01145709,0.00001240778,0.00002059892,0.0001916596,0.0001577015,0.9565219,0.001087984,0.02208532,0.006618823,0.0002393264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02923822,0.0005901483,0.9650745,0.001495957,0.001342215,0.00048997,0.00004136924,0.00004483691,0.001682798],"genre_scores_gemma":[0.5217568,0.00001222759,0.476993,0.0001690982,0.0001664506,0.00001328559,0.0000233475,0.00001647994,0.0008492412],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9556815,"threshold_uncertainty_score":0.9999384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4222998253043632,"score_gpt":0.5111461501127252,"score_spread":0.08884632480836202,"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."}}