{"id":"W2067567094","doi":"10.1111/j.0006-341x.2000.00496.x","title":"Marginal Models for Longitudinal Continuous Proportional Data","year":2000,"lang":"en","type":"article","venue":"Biometrics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Cancer Institute","keywords":"Applied mathematics; Mathematics; Zero (linguistics); Marginal model; Simplex; Longitudinal data; Function (biology); Statistics; Computer science; Combinatorics; Regression 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007239133,0.0001233002,0.0002482329,0.0003917902,0.0001063037,0.000062775,0.0003612399,0.00008754418,0.002871681],"category_scores_gemma":[0.00004791278,0.000147195,0.00007010762,0.0004615441,0.00005818582,0.0005232287,0.00006718025,0.00005662165,0.001111768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001275737,"about_ca_system_score_gemma":0.00001622207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006219838,"about_ca_topic_score_gemma":0.000002098304,"domain_scores_codex":[0.9986461,0.000004788655,0.0005033051,0.0005414568,0.00004859365,0.0002557998],"domain_scores_gemma":[0.9991906,0.00004687562,0.0001856664,0.0004882144,0.00001319834,0.00007545146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001491725,0.0008645552,0.6201561,0.00009280002,0.0002350689,0.000005536923,0.0001051322,0.003397635,0.0000215528,0.2140854,0.0594124,0.1014746],"study_design_scores_gemma":[0.001982507,0.0002496199,0.3406826,0.000008658088,0.00002972118,0.0000182248,0.00002917724,0.1775697,0.00002508844,0.06207222,0.4166733,0.00065919],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7252647,0.006832097,0.1977114,0.001188889,0.0009336747,0.001298249,0.006317586,0.0001187703,0.06033463],"genre_scores_gemma":[0.9773736,0.0006707303,0.01218611,0.0001498961,0.0002300307,0.00004697614,0.001198798,0.00002819001,0.008115693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3572609,"threshold_uncertainty_score":0.999666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3394569710516951,"score_gpt":0.2673015459231908,"score_spread":0.07215542512850437,"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."}}