{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006391817,0.000140831,0.0004150092,0.0001927862,0.0003113489,0.00005819707,0.0002832831,0.00004223353,0.004662424],"category_scores_gemma":[0.003780644,0.0001300544,0.00008890395,0.0001326038,0.00007048278,0.00006981483,0.00001845178,0.0003085136,9.291901e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002986567,"about_ca_system_score_gemma":0.00150364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002983553,"about_ca_topic_score_gemma":0.00141175,"domain_scores_codex":[0.9983515,0.00023764,0.0006807325,0.0001211342,0.0002936555,0.0003153603],"domain_scores_gemma":[0.9962329,0.002199635,0.0005220299,0.0001635706,0.0004488108,0.0004330554],"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":[0.00002659512,0.00001248428,0.00001224848,0.00006253119,0.00008388519,0.0001082409,0.0005172676,0.0006119879,0.00002177501,0.7664433,0.1659268,0.06617298],"study_design_scores_gemma":[0.0004955189,0.0003876468,0.00007997228,0.00002089553,0.0001168665,0.0001322296,0.0002711307,0.04423051,0.00002801964,0.9482671,0.005817713,0.0001523808],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01131699,0.00007677131,0.9598388,0.0001471504,0.0008269413,0.0002309584,0.02723544,0.000005505936,0.0003214373],"genre_scores_gemma":[0.08154342,0.000005321328,0.9176747,0.0003398423,0.0001333838,0.00001428396,0.00004533317,0.00003234521,0.0002113419],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1818238,"threshold_uncertainty_score":0.9962475,"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."}}