{"id":"W4306711481","doi":"10.1002/cjs.11739","title":"Automatic structure recovery for generalized additive models","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Poisson regression; Generalized additive model; Logistic regression; Computer science; Smoothing; Generalized linear model; Kernel (algebra); Kernel smoother; Monte Carlo method; Poisson distribution; Degree (music); Algorithm; Polynomial; Additive model; Mathematics; Kernel method; Artificial intelligence; Statistics; Machine learning; Discrete mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004404468,0.000890906,0.001265197,0.001785283,0.0006079337,0.0008861093,0.00188741,0.001164592,0.002035658],"category_scores_gemma":[0.01649737,0.0005920471,0.001511256,0.001235367,0.001253259,0.001418284,0.002704781,0.002486696,0.0007707818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005133132,"about_ca_system_score_gemma":0.001219829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002609235,"about_ca_topic_score_gemma":0.003340533,"domain_scores_codex":[0.9964179,0.00227353,0.0001094418,0.0004404963,0.0005839836,0.0001747919],"domain_scores_gemma":[0.9927604,0.004847414,0.0005867509,0.0009623062,0.0007029933,0.0001400874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001881994,0.0001520476,0.002816183,0.0001820787,0.0002445214,0.0003031633,0.0002784674,0.5750081,0.006347894,0.1313551,0.00426085,0.2788634],"study_design_scores_gemma":[0.000007734225,0.00001550847,0.0001739687,0.000007736659,0.000006772152,0.00002530524,0.00001049586,0.9587651,0.0006123012,0.03991682,0.0004477862,0.00001048815],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004259092,0.00005264696,0.9951907,0.00006774337,0.00000670564,0.000009143192,0.00001904902,0.0002228308,0.0001720324],"genre_scores_gemma":[0.3205831,0.0001944743,0.6754298,0.0001742533,0.00007306765,0.0001492882,0.0004125328,0.0003002053,0.002683168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004404468,"threshold_uncertainty_score":0.02329332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0890251362834438,"score_gpt":0.3177024671388063,"score_spread":0.2286773308553625,"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."}}