{"id":"W2132469784","doi":"10.1002/sim.3719","title":"Quadratic inference functions in marginal models for longitudinal data","year":2009,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Paul's Hospital; Alberta Cancer Foundation","funders":"National Science Foundation","keywords":"Inference; Computer science; Robustness (evolution); Statistical inference; Quadratic equation; Macro; Model selection; Applied mathematics; Econometrics; Machine learning; Statistics; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05256785,0.001747133,0.002316762,0.003147713,0.0009366719,0.002487658,0.003322074,0.002473968,0.007638624],"category_scores_gemma":[0.1430148,0.001355347,0.003414015,0.003342006,0.004253682,0.005297642,0.003671087,0.005789198,0.001833859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002493889,"about_ca_system_score_gemma":0.003319229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005673681,"about_ca_topic_score_gemma":0.003664494,"domain_scores_codex":[0.9717864,0.02201761,0.0008908593,0.002273978,0.002511292,0.0005198577],"domain_scores_gemma":[0.893611,0.09656991,0.002495728,0.003537636,0.003286281,0.0004995206],"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":[0.00009514749,0.00005175912,0.001697689,0.0005117233,0.0002447262,0.0001830612,0.0005062183,0.07304196,0.0004177548,0.8196739,0.005661885,0.09791417],"study_design_scores_gemma":[0.00002954092,0.00007056985,0.0008278948,0.0001321162,0.00007672172,0.0001311446,0.00004831245,0.3506883,0.0003926457,0.6369658,0.01058372,0.00005326754],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005900493,0.0006603176,0.997842,0.0002196302,0.00003363863,0.00002521837,0.00008234774,0.0001071111,0.0004397049],"genre_scores_gemma":[0.06632777,0.003878111,0.9210572,0.0007275249,0.00058883,0.00109262,0.0009146823,0.000607969,0.004805238],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05256785,"threshold_uncertainty_score":0.2780087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.331254591012354,"score_gpt":0.4930177444419592,"score_spread":0.1617631534296052,"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."}}