{"id":"W4385741753","doi":"10.1111/sjos.12681","title":"Marginal additive models for population‐averaged inference in longitudinal and cluster‐correlated data","year":2023,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Ecology and biodiversity studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Mathematics; Statistics; Cluster (spacecraft); Marginal model; Population; Nonlinear system; Computation; Econometrics; Regression analysis; Algorithm; Artificial intelligence; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004260537,0.00008246064,0.000169563,0.00008287204,0.0001716875,0.00001803547,0.0001926035,0.00004630605,0.0001026224],"category_scores_gemma":[0.000299498,0.00007404987,0.00001418979,0.0001814373,0.000144734,0.0003268247,0.0002547057,0.0001545479,0.00001585216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007100937,"about_ca_system_score_gemma":0.00001090886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009357775,"about_ca_topic_score_gemma":0.0006008721,"domain_scores_codex":[0.9992508,0.00004435941,0.0002301907,0.0001516547,0.0001428408,0.0001801172],"domain_scores_gemma":[0.9991888,0.0004661206,0.0001680194,0.00008727207,0.0000272587,0.000062494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001629488,0.00002572964,0.9784669,0.00001183959,0.00003033492,0.00009599538,0.0004392222,0.003743094,0.000004076991,0.0002504005,0.01281436,0.003955152],"study_design_scores_gemma":[0.0008319223,0.0001933323,0.948208,0.00004268379,0.0000416178,0.00002189765,0.0002489391,0.03992941,0.000001315538,0.01029915,0.00009077135,0.00009097247],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9333555,0.0000563217,0.06210894,0.0005203227,0.0003721781,0.0002869458,0.003069093,0.00001332231,0.0002174268],"genre_scores_gemma":[0.9904779,0.000174072,0.00896518,0.00003404204,0.00002102551,0.000001186155,0.0001603561,0.00000370355,0.0001625124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05712247,"threshold_uncertainty_score":0.3019666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05274869946504048,"score_gpt":0.2898129588525772,"score_spread":0.2370642593875367,"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."}}